lyken
/
nac3
forked from M-Labs/nac3
1
0
Fork 0
Code Pull Requests Activity

Compare commits

base: lyken:to-models-tmptmp
Branches Tags
lyken:ndarray-strides
lyken:to-models-tmptmp
lyken:to-models-save
lyken:ndstrides-wip
lyken:nds3
lyken:ndstrides-wip-9
lyken:ndstrides-wip-transpose
lyken:ndarray-strides-6
lyken:ndarray-strides-5
lyken:ndarray-strides-wip-2
lyken:ndarray-strides-wip
lyken:master
lyken:misc/impl-tracert
lyken:misc/mold
lyken:misc/update-pyo3
lyken:issue-313
lyken:refactor-primstore
lyken:numpy-anyall
lyken:enhance/cargo-test-standalone-demos
lyken:issue-136
lyken:rpc-obj-as-param
lyken:iterators
lyken:escape-analysis
lyken:refactor_anto
M-Labs:enhance/global-vars
M-Labs:master
M-Labs:misc/update-pyo3
M-Labs:misc/impl-tracert
M-Labs:misc/mold
M-Labs:refactor_composer
M-Labs:ndstrides-14-end
M-Labs:ndstrides-13-tests
M-Labs:ndstrides-12-builtins
M-Labs:ndstrides-11-matmul
M-Labs:ndstrides-10-ops
M-Labs:ndstrides-9-subassign
M-Labs:ndstrides-8-transpose
M-Labs:ndstrides-7-broadcasting
M-Labs:ndstrides-6-reshape
M-Labs:ndstrides-5-miscfuncs
M-Labs:ndstrides-4-nparray
M-Labs:ndstrides-3-indexing
M-Labs:ndstrides-2-basic
M-Labs:ndstrides-1-model
M-Labs:ndstrides
M-Labs:enhance/use-i1-everywhere
M-Labs:ndstrides-1-model-lifetime
M-Labs:ndstrides-enhance-irrt
M-Labs:ndstrides-intro
M-Labs:issue-313
M-Labs:refactor-primstore
M-Labs:numpy-anyall
M-Labs:enhance/cargo-test-standalone-demos
M-Labs:issue-136
M-Labs:rpc-obj-as-param
M-Labs:iterators
M-Labs:escape-analysis
M-Labs:refactor_anto
..
compare: M-Labs:master
Branches Tags
M-Labs:enhance/global-vars
M-Labs:master
M-Labs:misc/update-pyo3
M-Labs:misc/impl-tracert
M-Labs:misc/mold
M-Labs:refactor_composer
M-Labs:ndstrides-14-end
M-Labs:ndstrides-13-tests
M-Labs:ndstrides-12-builtins
M-Labs:ndstrides-11-matmul
M-Labs:ndstrides-10-ops
M-Labs:ndstrides-9-subassign
M-Labs:ndstrides-8-transpose
M-Labs:ndstrides-7-broadcasting
M-Labs:ndstrides-6-reshape
M-Labs:ndstrides-5-miscfuncs
M-Labs:ndstrides-4-nparray
M-Labs:ndstrides-3-indexing
M-Labs:ndstrides-2-basic
M-Labs:ndstrides-1-model
M-Labs:ndstrides
M-Labs:enhance/use-i1-everywhere
M-Labs:ndstrides-1-model-lifetime
M-Labs:ndstrides-enhance-irrt
M-Labs:ndstrides-intro
M-Labs:issue-313
M-Labs:refactor-primstore
M-Labs:numpy-anyall
M-Labs:enhance/cargo-test-standalone-demos
M-Labs:issue-136
M-Labs:rpc-obj-as-param
M-Labs:iterators
M-Labs:escape-analysis
M-Labs:refactor_anto
lyken:ndarray-strides
lyken:to-models-tmptmp
lyken:to-models-save
lyken:ndstrides-wip
lyken:nds3
lyken:ndstrides-wip-9
lyken:ndstrides-wip-transpose
lyken:ndarray-strides-6
lyken:ndarray-strides-5
lyken:ndarray-strides-wip-2
lyken:ndarray-strides-wip
lyken:master
lyken:misc/impl-tracert
lyken:misc/mold
lyken:misc/update-pyo3
lyken:issue-313
lyken:refactor-primstore
lyken:numpy-anyall
lyken:enhance/cargo-test-standalone-demos
lyken:issue-136
lyken:rpc-obj-as-param
lyken:iterators
lyken:escape-analysis
lyken:refactor_anto

42 Commits
to-models- ... master

Author SHA1 Message Date
mwojcik f2c047ba57 artiq: support async rpcs 
Co-authored-by: mwojcik <mw@m-labs.hk>
Co-committed-by: mwojcik <mw@m-labs.hk>
 2024-09-13 12:12:13 +08:00
David Mak 5e2e77a500 [meta] Bump inkwell to v0.5 2024-09-13 11:11:14 +08:00
David Mak f3cc4702b9 [meta] Update dependencies 2024-09-13 11:11:14 +08:00
David Mak 3e92c491f5 [standalone] Add tests creating ndarrays with tuple dims 2024-09-11 15:52:43 +08:00
lyken 7f629f1579 core: fix comment in unify_call 2024-09-11 15:46:19 +08:00
lyken 5640a793e2 core: allow np_full to take tuple shapes 2024-09-11 15:46:19 +08:00
David Mak abbaa506ad [standalone] Remove redundant recreation of TargetMachine 2024-09-09 14:27:10 +08:00
David Mak f3dc02d646 [meta] Apply cargo fmt 2024-09-09 14:24:52 +08:00
David Mak ea217eaea1 [meta] Update pre-commit config 
Directly invoke cargo using nix develop to avoid using the system cargo.
 2024-09-09 14:24:38 +08:00
Sébastien Bourdeauducq 5a34551905 allow the use of the LLVM shared library 
Which in turns allows working around the incompatibility of the LLVM static library
with Rust link-args=-rdynamic, which produces binaries that either fail to link (OpenBSD)
or segfault on startup (Linux).

The year is 2024 and compiler toolchains are still a trash fire like this.
 2024-09-09 11:17:31 +08:00
Sebastien Bourdeauducq 6098b1b853 fix previous commit 2024-09-06 11:32:08 +08:00
Sebastien Bourdeauducq 668ccb1c95 nac3core: expose inkwell and nac3parser 2024-09-06 11:06:26 +08:00
Sebastien Bourdeauducq a3c624d69d update all dependencies 2024-09-06 10:21:58 +08:00
Sébastien Bourdeauducq bd06155f34 irrt: compatibility with pre-C23 compilers 2024-09-05 18:54:55 +08:00
David Mak 9c33c4209c [core] Fix type of ndarray.element_type 
Should be the element type of the NDArray itself, not the pointer to its
type.
 2024-08-30 22:47:38 +08:00
Sebastien Bourdeauducq 122983f11c flake: update dependencies 2024-08-30 14:45:38 +08:00
David Mak 71c3a65a31 [core] codegen/stmt: Fix obtaining return type of sret functions 2024-08-29 19:15:30 +08:00
David Mak 8c540d1033 [core] codegen/stmt: Add more casts for boolean types 2024-08-29 16:36:32 +08:00
David Mak 0cc60a3d33 [core] codegen/expr: Fix missing cast to i1 2024-08-29 16:36:32 +08:00
David Mak a59c26aa99 [artiq] Fix RPC of ndarrays from host 2024-08-29 16:08:45 +08:00
David Mak 02d93b11d1 [meta] Update dependencies 2024-08-29 14:32:21 +08:00
lyken 59cad5bfe1
standalone: clang-format demo.c 2024-08-29 10:37:24 +08:00
lyken 4318f8de84
standalone: improve src/assignment.py 2024-08-29 10:33:58 +08:00
David Mak 15ac00708a [core] Use quoted include paths instead of angled brackets 
This is preferred for user-defined headers.
 2024-08-28 16:37:03 +08:00
lyken c8dfdcfdea
standalone & artiq: remove class_names from resolver 2024-08-27 23:43:40 +08:00
Sébastien Bourdeauducq 600a5c8679 Revert "standalone: reformat demo.c" 
This reverts commit 308edb8237.
 2024-08-27 23:06:49 +08:00
lyken 22c4d25802 core/typecheck: add missing typecheck in matmul 2024-08-27 22:59:39 +08:00
lyken 308edb8237 standalone: reformat demo.c 2024-08-27 22:55:22 +08:00
lyken 9848795dcc core/irrt: add exceptions and debug utils 2024-08-27 22:55:22 +08:00
lyken 58222feed4 core/irrt: split into headers 2024-08-27 22:55:22 +08:00
lyken 518f21d174 core/irrt: build.rs capture IR defined constants 2024-08-27 22:55:22 +08:00
lyken e8e49684bf core/irrt: build.rs capture IR defined types 2024-08-27 22:55:22 +08:00
lyken b2900b4883 core/irrt: use +std=c++20 to compile 
To explicitly set the C++ variant and avoid inconsistencies.
 2024-08-27 22:55:22 +08:00
lyken c6dade1394 core/irrt: reformat 2024-08-27 22:55:22 +08:00
lyken 7e3fcc0845 add .clang-format 2024-08-27 22:55:22 +08:00
lyken d3b4c60d7f core/irrt: comment build.rs & move irrt to nac3core/irrt 2024-08-27 22:55:22 +08:00
abdul124 5b2b6db7ed core: improve error messages 2024-08-26 18:37:55 +08:00
abdul124 15e62f467e standalone: add tests for polymorphism 2024-08-26 18:37:55 +08:00
abdul124 2c88924ff7 core: add support for simple polymorphism 2024-08-26 18:37:55 +08:00
abdul124 a744b139ba core: allow Call and AnnAssign in init block 2024-08-26 18:37:55 +08:00
David Mak 2b2b2dbf8f [core] Fix resolution of exception names in raise short form 
Previous implementation fails as `resolver.get_identifier_def` in ARTIQ
would return the exception __init__ function rather than the class.

We fix this by limiting the exception class resolution to only include
raise statements, and to force the exception name to always be treated
as a class.

Fixes #501.
 2024-08-26 18:35:02 +08:00
David Mak d9f96dab33 [core] Add codegen_unreachable 2024-08-23 13:10:55 +08:00
99 changed files with 7432 additions and 9442 deletions
Show Stats Expand all files Collapse all files

33
.clang-format
View File

@ -1,3 +1,32 @@
BasedOnStyle: Microsoft
BasedOnStyle: LLVM
Language: Cpp
Standard: Cpp11
AccessModifierOffset: -1
AlignEscapedNewlines: Left
AlwaysBreakAfterReturnType: None
AlwaysBreakTemplateDeclarations: Yes
AllowAllParametersOfDeclarationOnNextLine: false
AllowShortFunctionsOnASingleLine: Inline
BinPackParameters: false
BreakBeforeBinaryOperators: NonAssignment
BreakBeforeTernaryOperators: true
BreakConstructorInitializers: AfterColon
BreakInheritanceList: AfterColon
ColumnLimit: 120
ConstructorInitializerAllOnOneLineOrOnePerLine: true
ContinuationIndentWidth: 4
DerivePointerAlignment: false
IndentCaseLabels: true
IndentPPDirectives: None
IndentWidth: 4
ReflowComments: false
MaxEmptyLinesToKeep: 1
PointerAlignment: Left
ReflowComments: true
SortIncludes: false
SortUsingDeclarations: true
SpaceAfterTemplateKeyword: false
SpacesBeforeTrailingComments: 2
TabWidth: 4
UseTab: Never

8
.pre-commit-config.yaml
View File

@ -8,17 +8,17 @@ repos:
hooks:
- id: nac3-cargo-fmt
name: nac3 cargo format
entry: cargo
entry: nix
language: system
types: [file, rust]
pass_filenames: false
description: Runs cargo fmt on the codebase.
args: [fmt]
args: [develop, -c, cargo, fmt, --all]
- id: nac3-cargo-clippy
name: nac3 cargo clippy
entry: cargo
entry: nix
language: system
types: [file, rust]
pass_filenames: false
description: Runs cargo clippy on the codebase.
args: [clippy, --tests]
args: [develop, -c, cargo, clippy, --tests]

217
Cargo.lock generated
View File

@ -90,18 +90,18 @@ checksum = "0c4b4d0bd25bd0b74681c0ad21497610ce1b7c91b1022cd21c80c6fbdd9476b0"
[[package]]
name = "bit-set"
version = "0.5.3"
version = "0.6.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "0700ddab506f33b20a03b13996eccd309a48e5ff77d0d95926aa0210fb4e95f1"
checksum = "f0481a0e032742109b1133a095184ee93d88f3dc9e0d28a5d033dc77a073f44f"
dependencies = [
"bit-vec",
]
[[package]]
name = "bit-vec"
version = "0.6.3"
version = "0.7.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "349f9b6a179ed607305526ca489b34ad0a41aed5f7980fa90eb03160b69598fb"
checksum = "d2c54ff287cfc0a34f38a6b832ea1bd8e448a330b3e40a50859e6488bee07f22"
[[package]]
name = "bitflags"
@ -109,6 +109,15 @@ version = "2.6.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "b048fb63fd8b5923fc5aa7b340d8e156aec7ec02f0c78fa8a6ddc2613f6f71de"
[[package]]
name = "block-buffer"
version = "0.10.4"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "3078c7629b62d3f0439517fa394996acacc5cbc91c5a20d8c658e77abd503a71"
dependencies = [
"generic-array",
]
[[package]]
name = "byteorder"
version = "1.5.0"
@ -117,9 +126,9 @@ checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b"
[[package]]
name = "cc"
version = "1.1.13"
version = "1.1.18"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "72db2f7947ecee9b03b510377e8bb9077afa27176fdbff55c51027e976fdcc48"
checksum = "b62ac837cdb5cb22e10a256099b4fc502b1dfe560cb282963a974d7abd80e476"
dependencies = [
"shlex",
]
@ -132,9 +141,9 @@ checksum = "baf1de4339761588bc0619e3cbc0120ee582ebb74b53b4efbf79117bd2da40fd"
[[package]]
name = "clap"
version = "4.5.16"
version = "4.5.17"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "ed6719fffa43d0d87e5fd8caeab59be1554fb028cd30edc88fc4369b17971019"
checksum = "3e5a21b8495e732f1b3c364c9949b201ca7bae518c502c80256c96ad79eaf6ac"
dependencies = [
"clap_builder",
"clap_derive",
@ -142,9 +151,9 @@ dependencies = [
[[package]]
name = "clap_builder"
version = "4.5.15"
version = "4.5.17"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "216aec2b177652e3846684cbfe25c9964d18ec45234f0f5da5157b207ed1aab6"
checksum = "8cf2dd12af7a047ad9d6da2b6b249759a22a7abc0f474c1dae1777afa4b21a73"
dependencies = [
"anstream",
"anstyle",
@ -161,7 +170,7 @@ dependencies = [
"heck 0.5.0",
"proc-macro2",
"quote",
"syn 2.0.75",
"syn 2.0.77",
]
[[package]]
@ -188,6 +197,15 @@ dependencies = [
"windows-sys 0.52.0",
]
[[package]]
name = "cpufeatures"
version = "0.2.14"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "608697df725056feaccfa42cffdaeeec3fccc4ffc38358ecd19b243e716a78e0"
dependencies = [
"libc",
]
[[package]]
name = "crossbeam"
version = "0.8.4"
@ -245,10 +263,24 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "22ec99545bb0ed0ea7bb9b8e1e9122ea386ff8a48c0922e43f36d45ab09e0e80"
[[package]]
name = "crunchy"
version = "0.2.2"
name = "crypto-common"
version = "0.1.6"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "7a81dae078cea95a014a339291cec439d2f232ebe854a9d672b796c6afafa9b7"
checksum = "1bfb12502f3fc46cca1bb51ac28df9d618d813cdc3d2f25b9fe775a34af26bb3"
dependencies = [
"generic-array",
"typenum",
]
[[package]]
name = "digest"
version = "0.10.7"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "9ed9a281f7bc9b7576e61468ba615a66a5c8cfdff42420a70aa82701a3b1e292"
dependencies = [
"block-buffer",
"crypto-common",
]
[[package]]
name = "dirs-next"
@ -310,9 +342,9 @@ dependencies = [
[[package]]
name = "fastrand"
version = "2.1.0"
version = "2.1.1"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "9fc0510504f03c51ada170672ac806f1f105a88aa97a5281117e1ddc3368e51a"
checksum = "e8c02a5121d4ea3eb16a80748c74f5549a5665e4c21333c6098f283870fbdea6"
[[package]]
name = "fixedbitset"
@ -329,6 +361,16 @@ dependencies = [
"byteorder",
]
[[package]]
name = "generic-array"
version = "0.14.7"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "85649ca51fd72272d7821adaf274ad91c288277713d9c18820d8499a7ff69e9a"
dependencies = [
"typenum",
"version_check",
]
[[package]]
name = "getopts"
version = "0.2.21"
@ -388,9 +430,9 @@ dependencies = [
[[package]]
name = "indexmap"
version = "2.4.0"
version = "2.5.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "93ead53efc7ea8ed3cfb0c79fc8023fbb782a5432b52830b6518941cebe6505c"
checksum = "68b900aa2f7301e21c36462b170ee99994de34dff39a4a6a528e80e7376d07e5"
dependencies = [
"equivalent",
"hashbrown 0.14.5",
@ -404,9 +446,9 @@ checksum = "b248f5224d1d606005e02c97f5aa4e88eeb230488bcc03bc9ca4d7991399f2b5"
[[package]]
name = "inkwell"
version = "0.4.0"
version = "0.5.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "b597a7b2cdf279aeef6d7149071e35e4bc87c2cf05a5b7f2d731300bffe587ea"
checksum = "40fb405537710d51f6bdbc8471365ddd4cd6d3a3c3ad6e0c8291691031ba94b2"
dependencies = [
"either",
"inkwell_internals",
@ -418,13 +460,13 @@ dependencies = [
[[package]]
name = "inkwell_internals"
version = "0.9.0"
version = "0.10.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "4fa4d8d74483041a882adaa9a29f633253a66dde85055f0495c121620ac484b2"
checksum = "9dd28cfd4cfba665d47d31c08a6ba637eed16770abca2eccbbc3ca831fef1e44"
dependencies = [
"proc-macro2",
"quote",
"syn 2.0.75",
"syn 2.0.77",
]
[[package]]
@ -447,15 +489,6 @@ version = "1.70.1"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "7943c866cc5cd64cbc25b2e01621d07fa8eb2a1a23160ee81ce38704e97b8ecf"
[[package]]
name = "itertools"
version = "0.11.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "b1c173a5686ce8bfa551b3563d0c2170bf24ca44da99c7ca4bfdab5418c3fe57"
dependencies = [
"either",
]
[[package]]
name = "itertools"
version = "0.13.0"
@ -472,32 +505,41 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "49f1f14873335454500d59611f1cf4a4b0f786f9ac11f4312a78e4cf2566695b"
[[package]]
name = "lalrpop"
version = "0.20.2"
name = "keccak"
version = "0.1.5"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "55cb077ad656299f160924eb2912aa147d7339ea7d69e1b5517326fdcec3c1ca"
checksum = "ecc2af9a1119c51f12a14607e783cb977bde58bc069ff0c3da1095e635d70654"
dependencies = [
"cpufeatures",
]
[[package]]
name = "lalrpop"
version = "0.21.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "6e56f323e2d610628d1f5bdd39168a774674ac7989ed67011963bb3f71edd797"
dependencies = [
"ascii-canvas",
"bit-set",
"ena",
"itertools 0.11.0",
"itertools",
"lalrpop-util",
"petgraph",
"pico-args",
"regex",
"regex-syntax",
"sha3",
"string_cache",
"term",
"tiny-keccak",
"unicode-xid",
"walkdir",
]
[[package]]
name = "lalrpop-util"
version = "0.20.2"
version = "0.21.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "507460a910eb7b32ee961886ff48539633b788a36b65692b95f225b844c82553"
checksum = "108dc8f5dabad92c65a03523055577d847f5dcc00f3e7d3a68bc4d48e01d8fe1"
dependencies = [
"regex-automata",
]
@ -510,9 +552,9 @@ checksum = "bbd2bcb4c963f2ddae06a2efc7e9f3591312473c50c6685e1f298068316e66fe"
[[package]]
name = "libc"
version = "0.2.157"
version = "0.2.158"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "374af5f94e54fa97cf75e945cce8a6b201e88a1a07e688b47dfd2a59c66dbd86"
checksum = "d8adc4bb1803a324070e64a98ae98f38934d91957a99cfb3a43dcbc01bc56439"
[[package]]
name = "libloading"
@ -594,11 +636,9 @@ dependencies = [
name = "nac3artiq"
version = "0.1.0"
dependencies = [
"inkwell",
"itertools 0.13.0",
"itertools",
"nac3core",
"nac3ld",
"nac3parser",
"parking_lot",
"pyo3",
"tempfile",
@ -619,11 +659,11 @@ name = "nac3core"
version = "0.1.0"
dependencies = [
"crossbeam",
"indexmap 2.4.0",
"indexmap 2.5.0",
"indoc",
"inkwell",
"insta",
"itertools 0.13.0",
"itertools",
"nac3parser",
"parking_lot",
"rayon",
@ -661,9 +701,7 @@ name = "nac3standalone"
version = "0.1.0"
dependencies = [
"clap",
"inkwell",
"nac3core",
"nac3parser",
"parking_lot",
]
@ -709,7 +747,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "b4c5cc86750666a3ed20bdaf5ca2a0344f9c67674cae0515bec2da16fbaa47db"
dependencies = [
"fixedbitset",
"indexmap 2.4.0",
"indexmap 2.5.0",
]
[[package]]
@ -752,7 +790,7 @@ dependencies = [
"phf_shared 0.11.2",
"proc-macro2",
"quote",
"syn 2.0.75",
"syn 2.0.77",
]
[[package]]
@ -856,7 +894,7 @@ dependencies = [
"proc-macro2",
"pyo3-macros-backend",
"quote",
"syn 2.0.75",
"syn 2.0.77",
]
[[package]]
@ -869,14 +907,14 @@ dependencies = [
"proc-macro2",
"pyo3-build-config",
"quote",
"syn 2.0.75",
"syn 2.0.77",
]
[[package]]
name = "quote"
version = "1.0.36"
version = "1.0.37"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "0fa76aaf39101c457836aec0ce2316dbdc3ab723cdda1c6bd4e6ad4208acaca7"
checksum = "b5b9d34b8991d19d98081b46eacdd8eb58c6f2b201139f7c5f643cc155a633af"
dependencies = [
"proc-macro2",
]
@ -933,18 +971,18 @@ dependencies = [
[[package]]
name = "redox_syscall"
version = "0.5.3"
version = "0.5.4"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "2a908a6e00f1fdd0dfd9c0eb08ce85126f6d8bbda50017e74bc4a4b7d4a926a4"
checksum = "0884ad60e090bf1345b93da0a5de8923c93884cd03f40dfcfddd3b4bee661853"
dependencies = [
"bitflags",
]
[[package]]
name = "redox_users"
version = "0.4.5"
version = "0.4.6"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "bd283d9651eeda4b2a83a43c1c91b266c40fd76ecd39a50a8c630ae69dc72891"
checksum = "ba009ff324d1fc1b900bd1fdb31564febe58a8ccc8a6fdbb93b543d33b13ca43"
dependencies = [
"getrandom",
"libredox",
@ -989,9 +1027,9 @@ dependencies = [
[[package]]
name = "rustix"
version = "0.38.34"
version = "0.38.37"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "70dc5ec042f7a43c4a73241207cecc9873a06d45debb38b329f8541d85c2730f"
checksum = "8acb788b847c24f28525660c4d7758620a7210875711f79e7f663cc152726811"
dependencies = [
"bitflags",
"errno",
@ -1035,29 +1073,29 @@ checksum = "61697e0a1c7e512e84a621326239844a24d8207b4669b41bc18b32ea5cbf988b"
[[package]]
name = "serde"
version = "1.0.208"
version = "1.0.210"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "cff085d2cb684faa248efb494c39b68e522822ac0de72ccf08109abde717cfb2"
checksum = "c8e3592472072e6e22e0a54d5904d9febf8508f65fb8552499a1abc7d1078c3a"
dependencies = [
"serde_derive",
]
[[package]]
name = "serde_derive"
version = "1.0.208"
version = "1.0.210"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "24008e81ff7613ed8e5ba0cfaf24e2c2f1e5b8a0495711e44fcd4882fca62bcf"
checksum = "243902eda00fad750862fc144cea25caca5e20d615af0a81bee94ca738f1df1f"
dependencies = [
"proc-macro2",
"quote",
"syn 2.0.75",
"syn 2.0.77",
]
[[package]]
name = "serde_json"
version = "1.0.125"
version = "1.0.128"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "83c8e735a073ccf5be70aa8066aa984eaf2fa000db6c8d0100ae605b366d31ed"
checksum = "6ff5456707a1de34e7e37f2a6fd3d3f808c318259cbd01ab6377795054b483d8"
dependencies = [
"itoa",
"memchr",
@ -1077,6 +1115,16 @@ dependencies = [
"yaml-rust",
]
[[package]]
name = "sha3"
version = "0.10.8"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "75872d278a8f37ef87fa0ddbda7802605cb18344497949862c0d4dcb291eba60"
dependencies = [
"digest",
"keccak",
]
[[package]]
name = "shlex"
version = "1.3.0"
@ -1147,7 +1195,7 @@ dependencies = [
"proc-macro2",
"quote",
"rustversion",
"syn 2.0.75",
"syn 2.0.77",
]
[[package]]
@ -1163,9 +1211,9 @@ dependencies = [
[[package]]
name = "syn"
version = "2.0.75"
version = "2.0.77"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "f6af063034fc1935ede7be0122941bafa9bacb949334d090b77ca98b5817c7d9"
checksum = "9f35bcdf61fd8e7be6caf75f429fdca8beb3ed76584befb503b1569faee373ed"
dependencies = [
"proc-macro2",
"quote",
@ -1232,17 +1280,14 @@ checksum = "a4558b58466b9ad7ca0f102865eccc95938dca1a74a856f2b57b6629050da261"
dependencies = [
"proc-macro2",
"quote",
"syn 2.0.75",
"syn 2.0.77",
]
[[package]]
name = "tiny-keccak"
version = "2.0.2"
name = "typenum"
version = "1.17.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "2c9d3793400a45f954c52e73d068316d76b6f4e36977e3fcebb13a2721e80237"
dependencies = [
"crunchy",
]
checksum = "42ff0bf0c66b8238c6f3b578df37d0b7848e55df8577b3f74f92a69acceeb825"
[[package]]
name = "unic-char-property"
@ -1298,9 +1343,9 @@ dependencies = [
[[package]]
name = "unicode-ident"
version = "1.0.12"
version = "1.0.13"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "3354b9ac3fae1ff6755cb6db53683adb661634f67557942dea4facebec0fee4b"
checksum = "e91b56cd4cadaeb79bbf1a5645f6b4f8dc5bde8834ad5894a8db35fda9efa1fe"
[[package]]
name = "unicode-width"
@ -1310,15 +1355,15 @@ checksum = "0336d538f7abc86d282a4189614dfaa90810dfc2c6f6427eaf88e16311dd225d"
[[package]]
name = "unicode-xid"
version = "0.2.4"
version = "0.2.5"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "f962df74c8c05a667b5ee8bcf162993134c104e96440b663c8daa176dc772d8c"
checksum = "229730647fbc343e3a80e463c1db7f78f3855d3f3739bee0dda773c9a037c90a"
[[package]]
name = "unicode_names2"
version = "1.2.2"
version = "1.3.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "addeebf294df7922a1164f729fb27ebbbcea99cc32b3bf08afab62757f707677"
checksum = "d1673eca9782c84de5f81b82e4109dcfb3611c8ba0d52930ec4a9478f547b2dd"
dependencies = [
"phf",
"unicode_names2_generator",
@ -1326,9 +1371,9 @@ dependencies = [
[[package]]
name = "unicode_names2_generator"
version = "1.2.2"
version = "1.3.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "f444b8bba042fe3c1251ffaca35c603f2dc2ccc08d595c65a8c4f76f3e8426c0"
checksum = "b91e5b84611016120197efd7dc93ef76774f4e084cd73c9fb3ea4a86c570c56e"
dependencies = [
"getopts",
"log",
@ -1510,5 +1555,5 @@ checksum = "fa4f8080344d4671fb4e831a13ad1e68092748387dfc4f55e356242fae12ce3e"
dependencies = [
"proc-macro2",
"quote",
"syn 2.0.75",
"syn 2.0.77",
]

6
flake.lock
View File

@ -2,11 +2,11 @@
"nodes": {
"nixpkgs": {
"locked": {
"lastModified": 1723637854,
"narHash": "sha256-med8+5DSWa2UnOqtdICndjDAEjxr5D7zaIiK4pn0Q7c=",
"lastModified": 1725432240,
"narHash": "sha256-+yj+xgsfZaErbfYM3T+QvEE2hU7UuE+Jf0fJCJ8uPS0=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "c3aa7b8938b17aebd2deecf7be0636000d62a2b9",
"rev": "ad416d066ca1222956472ab7d0555a6946746a80",
"type": "github"
},
"original": {

6
nac3artiq/Cargo.toml
View File

@ -13,15 +13,9 @@ itertools = "0.13"
pyo3 = { version = "0.21", features = ["extension-module", "gil-refs"] }
parking_lot = "0.12"
tempfile = "3.10"
nac3parser = { path = "../nac3parser" }
nac3core = { path = "../nac3core" }
nac3ld = { path = "../nac3ld" }
[dependencies.inkwell]
version = "0.4"
default-features = false
features = ["llvm14-0", "target-x86", "target-arm", "target-riscv", "no-libffi-linking"]
[features]
init-llvm-profile = []
no-escape-analysis = ["nac3core/no-escape-analysis"]

13
nac3artiq/demo/min_artiq.py
View File

@ -112,10 +112,15 @@ def extern(function):
register_function(function)
return function
def rpc(function):
"""Decorates a function declaration defined by the core device runtime."""
register_function(function)
return function
def rpc(arg=None, flags={}):
"""Decorates a function or method to be executed on the host interpreter."""
if arg is None:
def inner_decorator(function):
return rpc(function, flags)
return inner_decorator
register_function(arg)
return arg
def kernel(function_or_method):
"""Decorates a function or method to be executed on the core device."""

580
nac3artiq/src/codegen.rs
View File

@ -1,10 +1,12 @@
use nac3core::{
codegen::{
classes::{ListValue, UntypedArrayLikeAccessor},
expr::gen_call,
llvm_intrinsics::{call_int_smax, call_stackrestore, call_stacksave},
model::*,
object::{any::AnyObject, ndarray::NDArrayObject, range::RangeObject, str::str_model},
classes::{
ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayType,
NDArrayValue, ProxyType, ProxyValue, RangeValue, UntypedArrayLikeAccessor,
},
expr::{destructure_range, gen_call},
irrt::call_ndarray_calc_size,
llvm_intrinsics::{call_int_smax, call_memcpy_generic, call_stackrestore, call_stacksave},
stmt::{gen_block, gen_for_callback_incrementing, gen_if_callback, gen_with},
CodeGenContext, CodeGenerator,
},
@ -13,14 +15,14 @@ use nac3core::{
typecheck::typedef::{iter_type_vars, FunSignature, FuncArg, Type, TypeEnum, VarMap},
};
use nac3parser::ast::{Expr, ExprKind, Located, Stmt, StmtKind, StrRef};
use nac3core::nac3parser::ast::{Expr, ExprKind, Located, Stmt, StmtKind, StrRef};
use inkwell::{
use nac3core::inkwell::{
context::Context,
module::Linkage,
types::IntType,
types::{BasicType, IntType},
values::{BasicValueEnum, PointerValue, StructValue},
AddressSpace, IntPredicate,
AddressSpace, IntPredicate, OptimizationLevel,
};
use pyo3::{
@ -31,6 +33,7 @@ use pyo3::{
use crate::{symbol_resolver::InnerResolver, timeline::TimeFns};
use itertools::Itertools;
use nac3core::inkwell::values::IntValue;
use std::{
collections::{hash_map::DefaultHasher, HashMap},
hash::{Hash, Hasher},
@ -454,41 +457,55 @@ fn format_rpc_arg<'ctx>(
// NAC3: NDArray = { usize, usize*, T* }
// libproto_artiq: NDArray = [data[..], dim_sz[..]]
let ndarray = AnyObject { ty: arg_ty, value: arg };
let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
let llvm_i1 = ctx.ctx.bool_type();
let llvm_usize = generator.get_size_type(ctx.ctx);
let dtype = ctx.get_llvm_type(generator, ndarray.dtype);
let ndims = ndarray.ndims_llvm(generator, ctx.ctx);
let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, arg_ty);
let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
let llvm_arg_ty = NDArrayType::new(generator, ctx.ctx, llvm_elem_ty);
let llvm_arg = NDArrayValue::from_ptr_val(arg.into_pointer_value(), llvm_usize, None);
// `ndarray.data` is possibly not contiguous, and we need it to be contiguous for
// the reader.
let carray = ndarray.make_contiguous_ndarray(generator, ctx, Any(dtype));
let llvm_usize_sizeof = ctx
.builder
.build_int_truncate_or_bit_cast(llvm_arg_ty.size_type().size_of(), llvm_usize, "")
.unwrap();
let llvm_pdata_sizeof = ctx
.builder
.build_int_truncate_or_bit_cast(
llvm_elem_ty.ptr_type(AddressSpace::default()).size_of(),
llvm_usize,
"",
)
.unwrap();
let sizeof_sizet = Int(SizeT).sizeof(generator, ctx.ctx);
let sizeof_sizet = Int(SizeT).truncate_or_bit_cast(generator, ctx, sizeof_sizet);
let dims_buf_sz =
ctx.builder.build_int_mul(llvm_arg.load_ndims(ctx), llvm_usize_sizeof, "").unwrap();
let sizeof_pdata = Ptr(Any(dtype)).sizeof(generator, ctx.ctx);
let sizeof_pdata = Int(SizeT).truncate_or_bit_cast(generator, ctx, sizeof_pdata);
let buffer_size =
ctx.builder.build_int_add(dims_buf_sz, llvm_pdata_sizeof, "").unwrap();
let sizeof_buf_shape = sizeof_sizet.mul(ctx, ndims);
let sizeof_buf = sizeof_buf_shape.add(ctx, sizeof_pdata);
let buffer = ctx.builder.build_array_alloca(llvm_i8, buffer_size, "rpc.arg").unwrap();
let buffer = ArraySliceValue::from_ptr_val(buffer, buffer_size, Some("rpc.arg"));
// buf = { data: void*, shape: [size_t; ndims]; }
let buf = Int(Byte).array_alloca(generator, ctx, sizeof_buf.value);
let buf_data = buf;
let buf_shape = buf_data.offset(ctx, sizeof_pdata.value);
call_memcpy_generic(
ctx,
buffer.base_ptr(ctx, generator),
llvm_arg.ptr_to_data(ctx),
llvm_pdata_sizeof,
llvm_i1.const_zero(),
);
// Write to `buf->data`
let carray_data = carray.get(generator, ctx, |f| f.data); // has type Ptr<Any>
let carray_data = carray_data.pointer_cast(generator, ctx, Int(Byte));
buf_data.copy_from(generator, ctx, carray_data, sizeof_pdata.value);
let pbuffer_dims_begin =
unsafe { buffer.ptr_offset_unchecked(ctx, generator, &llvm_pdata_sizeof, None) };
call_memcpy_generic(
ctx,
pbuffer_dims_begin,
llvm_arg.dim_sizes().base_ptr(ctx, generator),
dims_buf_sz,
llvm_i1.const_zero(),
);
// Write to `buf->shape`
let carray_shape = ndarray.instance.get(generator, ctx, |f| f.shape);
let carray_shape_i8 = carray_shape.pointer_cast(generator, ctx, Int(Byte));
buf_shape.copy_from(generator, ctx, carray_shape_i8, sizeof_buf_shape.value);
buf.value
buffer.base_ptr(ctx, generator)
}
_ => {
@ -498,7 +515,7 @@ fn format_rpc_arg<'ctx>(
ctx.builder.build_store(arg_slot, arg).unwrap();
ctx.builder
.build_bitcast(arg_slot, llvm_pi8, "rpc.arg")
.build_bit_cast(arg_slot, llvm_pi8, "rpc.arg")
.map(BasicValueEnum::into_pointer_value)
.unwrap()
}
@ -509,12 +526,305 @@ fn format_rpc_arg<'ctx>(
arg_slot
}
/// Formats an RPC return value to conform to the expected format required by NAC3.
fn format_rpc_ret<'ctx>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, '_>,
ret_ty: Type,
) -> Option<BasicValueEnum<'ctx>> {
// -- receive value:
// T result = {
// void *ret_ptr = alloca(sizeof(T));
// void *ptr = ret_ptr;
// loop: int size = rpc_recv(ptr);
// // Non-zero: Provide `size` bytes of extra storage for variable-length data.
// if(size) { ptr = alloca(size); goto loop; }
// else *(T*)ret_ptr
// }
let llvm_i8 = ctx.ctx.i8_type();
let llvm_i32 = ctx.ctx.i32_type();
let llvm_i8_8 = ctx.ctx.struct_type(&[llvm_i8.array_type(8).into()], false);
let llvm_pi8 = llvm_i8.ptr_type(AddressSpace::default());
let rpc_recv = ctx.module.get_function("rpc_recv").unwrap_or_else(|| {
ctx.module.add_function("rpc_recv", llvm_i32.fn_type(&[llvm_pi8.into()], false), None)
});
if ctx.unifier.unioned(ret_ty, ctx.primitives.none) {
ctx.build_call_or_invoke(rpc_recv, &[llvm_pi8.const_null().into()], "rpc_recv");
return None;
}
let prehead_bb = ctx.builder.get_insert_block().unwrap();
let current_function = prehead_bb.get_parent().unwrap();
let head_bb = ctx.ctx.append_basic_block(current_function, "rpc.head");
let alloc_bb = ctx.ctx.append_basic_block(current_function, "rpc.continue");
let tail_bb = ctx.ctx.append_basic_block(current_function, "rpc.tail");
let llvm_ret_ty = ctx.get_llvm_abi_type(generator, ret_ty);
let result = match &*ctx.unifier.get_ty_immutable(ret_ty) {
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
let llvm_i1 = ctx.ctx.bool_type();
let llvm_usize = generator.get_size_type(ctx.ctx);
// Round `val` up to its modulo `power_of_two`
let round_up = |ctx: &mut CodeGenContext<'ctx, '_>,
val: IntValue<'ctx>,
power_of_two: IntValue<'ctx>| {
debug_assert_eq!(
val.get_type().get_bit_width(),
power_of_two.get_type().get_bit_width()
);
let llvm_val_t = val.get_type();
let max_rem = ctx
.builder
.build_int_sub(power_of_two, llvm_val_t.const_int(1, false), "")
.unwrap();
ctx.builder
.build_and(
ctx.builder.build_int_add(val, max_rem, "").unwrap(),
ctx.builder.build_not(max_rem, "").unwrap(),
"",
)
.unwrap()
};
// Setup types
let (elem_ty, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ret_ty);
let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
let llvm_ret_ty = NDArrayType::new(generator, ctx.ctx, llvm_elem_ty);
// Allocate the resulting ndarray
// A condition after format_rpc_ret ensures this will not be popped this off.
let ndarray = llvm_ret_ty.new_value(generator, ctx, Some("rpc.result"));
// Setup ndims
let ndims =
if let TypeEnum::TLiteral { values, .. } = &*ctx.unifier.get_ty_immutable(ndims) {
assert_eq!(values.len(), 1);
u64::try_from(values[0].clone()).unwrap()
} else {
unreachable!();
};
// Set `ndarray.ndims`
ndarray.store_ndims(ctx, generator, llvm_usize.const_int(ndims, false));
// Allocate `ndarray.shape` [size_t; ndims]
ndarray.create_dim_sizes(ctx, llvm_usize, ndarray.load_ndims(ctx));
/*
ndarray now:
- .ndims: initialized
- .shape: allocated but uninitialized .shape
- .data: uninitialized
*/
let llvm_usize_sizeof = ctx
.builder
.build_int_truncate_or_bit_cast(llvm_usize.size_of(), llvm_usize, "")
.unwrap();
let llvm_pdata_sizeof = ctx
.builder
.build_int_truncate_or_bit_cast(
llvm_elem_ty.ptr_type(AddressSpace::default()).size_of(),
llvm_usize,
"",
)
.unwrap();
let llvm_elem_sizeof = ctx
.builder
.build_int_truncate_or_bit_cast(llvm_elem_ty.size_of().unwrap(), llvm_usize, "")
.unwrap();
// Allocates a buffer for the initial RPC'ed object, which is guaranteed to be
// (4 + 4 * ndims) bytes with 8-byte alignment
let sizeof_dims =
ctx.builder.build_int_mul(ndarray.load_ndims(ctx), llvm_usize_sizeof, "").unwrap();
let unaligned_buffer_size =
ctx.builder.build_int_add(sizeof_dims, llvm_pdata_sizeof, "").unwrap();
let buffer_size = round_up(ctx, unaligned_buffer_size, llvm_usize.const_int(8, false));
let stackptr = call_stacksave(ctx, None);
// Just to be absolutely sure, alloca in [i8 x 8] slices to force 8-byte alignment
let buffer = ctx
.builder
.build_array_alloca(
llvm_i8_8,
ctx.builder
.build_int_unsigned_div(buffer_size, llvm_usize.const_int(8, false), "")
.unwrap(),
"rpc.buffer",
)
.unwrap();
let buffer = ctx
.builder
.build_bit_cast(buffer, llvm_pi8, "")
.map(BasicValueEnum::into_pointer_value)
.unwrap();
let buffer = ArraySliceValue::from_ptr_val(buffer, buffer_size, None);
// The first call to `rpc_recv` reads the top-level ndarray object: [pdata, shape]
//
// The returned value is the number of bytes for `ndarray.data`.
let ndarray_nbytes = ctx
.build_call_or_invoke(
rpc_recv,
&[buffer.base_ptr(ctx, generator).into()], // Reads [usize; ndims]. NOTE: We are allocated [size_t; ndims].
"rpc.size.next",
)
.map(BasicValueEnum::into_int_value)
.unwrap();
// debug_assert(ndarray_nbytes > 0)
if ctx.registry.llvm_options.opt_level == OptimizationLevel::None {
ctx.make_assert(
generator,
ctx.builder
.build_int_compare(
IntPredicate::UGT,
ndarray_nbytes,
ndarray_nbytes.get_type().const_zero(),
"",
)
.unwrap(),
"0:AssertionError",
"Unexpected RPC termination for ndarray - Expected data buffer next",
[None, None, None],
ctx.current_loc,
);
}
// Copy shape from the buffer to `ndarray.shape`.
let pbuffer_dims =
unsafe { buffer.ptr_offset_unchecked(ctx, generator, &llvm_pdata_sizeof, None) };
call_memcpy_generic(
ctx,
ndarray.dim_sizes().base_ptr(ctx, generator),
pbuffer_dims,
sizeof_dims,
llvm_i1.const_zero(),
);
// Restore stack from before allocation of buffer
call_stackrestore(ctx, stackptr);
// Allocate `ndarray.data`.
// `ndarray.shape` must be initialized beforehand in this implementation
// (for ndarray.create_data() to know how many elements to allocate)
let num_elements =
call_ndarray_calc_size(generator, ctx, &ndarray.dim_sizes(), (None, None));
// debug_assert(nelems * sizeof(T) >= ndarray_nbytes)
if ctx.registry.llvm_options.opt_level == OptimizationLevel::None {
let sizeof_data =
ctx.builder.build_int_mul(num_elements, llvm_elem_sizeof, "").unwrap();
ctx.make_assert(
generator,
ctx.builder.build_int_compare(IntPredicate::UGE,
sizeof_data,
ndarray_nbytes,
"",
).unwrap(),
"0:AssertionError",
"Unexpected allocation size request for ndarray data - Expected up to {0} bytes, got {1} bytes",
[Some(sizeof_data), Some(ndarray_nbytes), None],
ctx.current_loc,
);
}
ndarray.create_data(ctx, llvm_elem_ty, num_elements);
let ndarray_data = ndarray.data().base_ptr(ctx, generator);
let ndarray_data_i8 =
ctx.builder.build_pointer_cast(ndarray_data, llvm_pi8, "").unwrap();
// NOTE: Currently on `prehead_bb`
ctx.builder.build_unconditional_branch(head_bb).unwrap();
// Inserting into `head_bb`. Do `rpc_recv` for `data` recursively.
ctx.builder.position_at_end(head_bb);
let phi = ctx.builder.build_phi(llvm_pi8, "rpc.ptr").unwrap();
phi.add_incoming(&[(&ndarray_data_i8, prehead_bb)]);
let alloc_size = ctx
.build_call_or_invoke(rpc_recv, &[phi.as_basic_value()], "rpc.size.next")
.map(BasicValueEnum::into_int_value)
.unwrap();
let is_done = ctx
.builder
.build_int_compare(IntPredicate::EQ, llvm_i32.const_zero(), alloc_size, "rpc.done")
.unwrap();
ctx.builder.build_conditional_branch(is_done, tail_bb, alloc_bb).unwrap();
ctx.builder.position_at_end(alloc_bb);
// Align the allocation to sizeof(T)
let alloc_size = round_up(ctx, alloc_size, llvm_elem_sizeof);
let alloc_ptr = ctx
.builder
.build_array_alloca(
llvm_elem_ty,
ctx.builder.build_int_unsigned_div(alloc_size, llvm_elem_sizeof, "").unwrap(),
"rpc.alloc",
)
.unwrap();
let alloc_ptr =
ctx.builder.build_pointer_cast(alloc_ptr, llvm_pi8, "rpc.alloc.ptr").unwrap();
phi.add_incoming(&[(&alloc_ptr, alloc_bb)]);
ctx.builder.build_unconditional_branch(head_bb).unwrap();
ctx.builder.position_at_end(tail_bb);
ndarray.as_base_value().into()
}
_ => {
let slot = ctx.builder.build_alloca(llvm_ret_ty, "rpc.ret.slot").unwrap();
let slotgen = ctx.builder.build_bit_cast(slot, llvm_pi8, "rpc.ret.ptr").unwrap();
ctx.builder.build_unconditional_branch(head_bb).unwrap();
ctx.builder.position_at_end(head_bb);
let phi = ctx.builder.build_phi(llvm_pi8, "rpc.ptr").unwrap();
phi.add_incoming(&[(&slotgen, prehead_bb)]);
let alloc_size = ctx
.build_call_or_invoke(rpc_recv, &[phi.as_basic_value()], "rpc.size.next")
.unwrap()
.into_int_value();
let is_done = ctx
.builder
.build_int_compare(IntPredicate::EQ, llvm_i32.const_zero(), alloc_size, "rpc.done")
.unwrap();
ctx.builder.build_conditional_branch(is_done, tail_bb, alloc_bb).unwrap();
ctx.builder.position_at_end(alloc_bb);
let alloc_ptr =
ctx.builder.build_array_alloca(llvm_pi8, alloc_size, "rpc.alloc").unwrap();
let alloc_ptr =
ctx.builder.build_bit_cast(alloc_ptr, llvm_pi8, "rpc.alloc.ptr").unwrap();
phi.add_incoming(&[(&alloc_ptr, alloc_bb)]);
ctx.builder.build_unconditional_branch(head_bb).unwrap();
ctx.builder.position_at_end(tail_bb);
ctx.builder.build_load(slot, "rpc.result").unwrap()
}
};
Some(result)
}
fn rpc_codegen_callback_fn<'ctx>(
ctx: &mut CodeGenContext<'ctx, '_>,
obj: Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: Vec<(Option<StrRef>, ValueEnum<'ctx>)>,
generator: &mut dyn CodeGenerator,
is_async: bool,
) -> Result<Option<BasicValueEnum<'ctx>>, String> {
let int8 = ctx.ctx.i8_type();
let int32 = ctx.ctx.i32_type();
@ -623,6 +933,29 @@ fn rpc_codegen_callback_fn<'ctx>(
}
// call
if is_async {
let rpc_send_async = ctx.module.get_function("rpc_send_async").unwrap_or_else(|| {
ctx.module.add_function(
"rpc_send_async",
ctx.ctx.void_type().fn_type(
&[
int32.into(),
tag_ptr_type.ptr_type(AddressSpace::default()).into(),
ptr_type.ptr_type(AddressSpace::default()).into(),
],
false,
),
None,
)
});
ctx.builder
.build_call(
rpc_send_async,
&[service_id.into(), tag_ptr.into(), args_ptr.into()],
"rpc.send",
)
.unwrap();
} else {
let rpc_send = ctx.module.get_function("rpc_send").unwrap_or_else(|| {
ctx.module.add_function(
"rpc_send",
@ -640,67 +973,24 @@ fn rpc_codegen_callback_fn<'ctx>(
ctx.builder
.build_call(rpc_send, &[service_id.into(), tag_ptr.into(), args_ptr.into()], "rpc.send")
.unwrap();
}
// reclaim stack space used by arguments
call_stackrestore(ctx, stackptr);
// -- receive value:
// T result = {
// void *ret_ptr = alloca(sizeof(T));
// void *ptr = ret_ptr;
// loop: int size = rpc_recv(ptr);
// // Non-zero: Provide `size` bytes of extra storage for variable-length data.
// if(size) { ptr = alloca(size); goto loop; }
// else *(T*)ret_ptr
// }
let rpc_recv = ctx.module.get_function("rpc_recv").unwrap_or_else(|| {
ctx.module.add_function("rpc_recv", int32.fn_type(&[ptr_type.into()], false), None)
});
if is_async {
// async RPCs do not return any values
Ok(None)
} else {
let result = format_rpc_ret(generator, ctx, fun.0.ret);
if ctx.unifier.unioned(fun.0.ret, ctx.primitives.none) {
ctx.build_call_or_invoke(rpc_recv, &[ptr_type.const_null().into()], "rpc_recv");
return Ok(None);
}
let prehead_bb = ctx.builder.get_insert_block().unwrap();
let current_function = prehead_bb.get_parent().unwrap();
let head_bb = ctx.ctx.append_basic_block(current_function, "rpc.head");
let alloc_bb = ctx.ctx.append_basic_block(current_function, "rpc.continue");
let tail_bb = ctx.ctx.append_basic_block(current_function, "rpc.tail");
let ret_ty = ctx.get_llvm_abi_type(generator, fun.0.ret);
let need_load = !ret_ty.is_pointer_type();
let slot = ctx.builder.build_alloca(ret_ty, "rpc.ret.slot").unwrap();
let slotgen = ctx.builder.build_bitcast(slot, ptr_type, "rpc.ret.ptr").unwrap();
ctx.builder.build_unconditional_branch(head_bb).unwrap();
ctx.builder.position_at_end(head_bb);
let phi = ctx.builder.build_phi(ptr_type, "rpc.ptr").unwrap();
phi.add_incoming(&[(&slotgen, prehead_bb)]);
let alloc_size = ctx
.build_call_or_invoke(rpc_recv, &[phi.as_basic_value()], "rpc.size.next")
.unwrap()
.into_int_value();
let is_done = ctx
.builder
.build_int_compare(inkwell::IntPredicate::EQ, int32.const_zero(), alloc_size, "rpc.done")
.unwrap();
ctx.builder.build_conditional_branch(is_done, tail_bb, alloc_bb).unwrap();
ctx.builder.position_at_end(alloc_bb);
let alloc_ptr = ctx.builder.build_array_alloca(ptr_type, alloc_size, "rpc.alloc").unwrap();
let alloc_ptr = ctx.builder.build_bitcast(alloc_ptr, ptr_type, "rpc.alloc.ptr").unwrap();
phi.add_incoming(&[(&alloc_ptr, alloc_bb)]);
ctx.builder.build_unconditional_branch(head_bb).unwrap();
ctx.builder.position_at_end(tail_bb);
let result = ctx.builder.build_load(slot, "rpc.result").unwrap();
if need_load {
if !result.is_some_and(|res| res.get_type().is_pointer_type()) {
// An RPC returning an NDArray would not touch here.
call_stackrestore(ctx, stackptr);
}
Ok(Some(result))
Ok(result)
}
}
pub fn attributes_writeback(
@ -795,7 +1085,7 @@ pub fn attributes_writeback(
let args: Vec<_> =
values.into_iter().map(|(_, val)| (None, ValueEnum::Dynamic(val))).collect();
if let Err(e) =
rpc_codegen_callback_fn(ctx, None, (&fun, PrimDef::Int32.id()), args, generator)
rpc_codegen_callback_fn(ctx, None, (&fun, PrimDef::Int32.id()), args, generator, false)
{
return Ok(Err(e));
}
@ -805,9 +1095,9 @@ pub fn attributes_writeback(
Ok(())
}
pub fn rpc_codegen_callback() -> Arc<GenCall> {
Arc::new(GenCall::new(Box::new(|ctx, obj, fun, args, generator| {
rpc_codegen_callback_fn(ctx, obj, fun, args, generator)
pub fn rpc_codegen_callback(is_async: bool) -> Arc<GenCall> {
Arc::new(GenCall::new(Box::new(move |ctx, obj, fun, args, generator| {
rpc_codegen_callback_fn(ctx, obj, fun, args, generator, is_async)
})))
}
@ -877,12 +1167,12 @@ fn polymorphic_print<'ctx>(
});
let fmt = ctx.gen_string(generator, fmt);
let fmt = fmt.get_field(generator, ctx.ctx, |f| f.base);
let fmt = unsafe { fmt.get_field_at_index_unchecked(0) }.into_pointer_value();
ctx.builder
.build_call(
print_fn,
&once(fmt.value.into()).chain(args).map(BasicValueEnum::into).collect_vec(),
&once(fmt.into()).chain(args).map(BasicValueEnum::into).collect_vec(),
"",
)
.unwrap();
@ -957,23 +1247,20 @@ fn polymorphic_print<'ctx>(
fmt.push_str("%.*s");
let true_str = ctx.gen_string(generator, "True");
let true_data =
unsafe { true_str.get_field_at_index_unchecked(0) }.into_pointer_value();
let true_len = unsafe { true_str.get_field_at_index_unchecked(1) }.into_int_value();
let false_str = ctx.gen_string(generator, "False");
let true_data = true_str.get_field(generator, ctx.ctx, |f| f.base);
let true_len = true_str.get_field(generator, ctx.ctx, |f| f.len);
let false_data = false_str.get_field(generator, ctx.ctx, |f| f.base);
let false_len = false_str.get_field(generator, ctx.ctx, |f| f.len);
let false_data =
unsafe { false_str.get_field_at_index_unchecked(0) }.into_pointer_value();
let false_len =
unsafe { false_str.get_field_at_index_unchecked(1) }.into_int_value();
let bool_val = generator.bool_to_i1(ctx, value.into_int_value());
args.extend([
ctx.builder
.build_select(bool_val, true_len.value, false_len.value, "")
.unwrap(),
ctx.builder
.build_select(bool_val, true_data.value, false_data.value, "")
.unwrap(),
ctx.builder.build_select(bool_val, true_len, false_len, "").unwrap(),
ctx.builder.build_select(bool_val, true_data, false_data, "").unwrap(),
]);
}
@ -1011,12 +1298,11 @@ fn polymorphic_print<'ctx>(
fmt.push_str("%.*s");
}
let str = str_model().check_value(generator, ctx.ctx, value).unwrap();
let str = value.into_struct_value();
let str_data = unsafe { str.get_field_at_index_unchecked(0) }.into_pointer_value();
let str_len = unsafe { str.get_field_at_index_unchecked(1) }.into_int_value();
let str_data = str.get_field(generator, ctx.ctx, |f| f.base);
let str_len = str.get_field(generator, ctx.ctx, |f| f.len);
args.extend(&[str_len.value.into(), str_data.value.into()]);
args.extend(&[str_len.into(), str_data.into()]);
}
TypeEnum::TObj { obj_id, params, .. } if *obj_id == PrimDef::List.id() => {
@ -1076,46 +1362,56 @@ fn polymorphic_print<'ctx>(
}
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
fmt.push_str("array([");
flush(ctx, generator, &mut fmt, &mut args);
let ndarray = AnyObject { ty, value };
let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
let val = NDArrayValue::from_ptr_val(value.into_pointer_value(), llvm_usize, None);
let len = call_ndarray_calc_size(generator, ctx, &val.dim_sizes(), (None, None));
let last =
ctx.builder.build_int_sub(len, llvm_usize.const_int(1, false), "").unwrap();
let num_0 = Int(SizeT).const_0(generator, ctx.ctx);
// Print `ndarray` as a flat list delimited by interspersed with ", \0"
ndarray.foreach(generator, ctx, |generator, ctx, _, hdl| {
let i = hdl.get_index(generator, ctx);
let scalar = hdl.get_scalar(generator, ctx);
// if (i != 0) { puts(", "); }
gen_if_callback(
gen_for_callback_incrementing(
generator,
ctx,
|_, ctx| {
let not_first = i.compare(ctx, IntPredicate::NE, num_0);
Ok(not_first.value)
},
|generator, ctx| {
printf(ctx, generator, ", \0".into(), Vec::default());
Ok(())
},
|_, _| Ok(()),
)?;
None,
llvm_usize.const_zero(),
(len, false),
|generator, ctx, _, i| {
let elem = unsafe { val.data().get_unchecked(ctx, generator, &i, None) };
// Print element
polymorphic_print(
ctx,
generator,
&[(scalar.ty, scalar.value.into())],
&[(elem_ty, elem.into())],
"",
None,
true,
as_rtio,
)?;
gen_if_callback(
generator,
ctx,
|_, ctx| {
Ok(ctx
.builder
.build_int_compare(IntPredicate::ULT, i, last, "")
.unwrap())
},
|generator, ctx| {
printf(ctx, generator, ", \0".into(), Vec::default());
Ok(())
})?;
},
|_, _| Ok(()),
)?;
Ok(())
},
llvm_usize.const_int(1, false),
)?;
fmt.push_str(")]");
flush(ctx, generator, &mut fmt, &mut args);
@ -1125,13 +1421,9 @@ fn polymorphic_print<'ctx>(
fmt.push_str("range(");
flush(ctx, generator, &mut fmt, &mut args);
let range = AnyObject { ty, value };
let range = RangeObject::from_object(generator, ctx, range);
let val = RangeValue::from_ptr_val(value.into_pointer_value(), None);
let (start, stop, step) = range.instance.destructure(generator, ctx);
let start = start.value;
let stop = stop.value;
let step = step.value;
let (start, stop, step) = destructure_range(ctx, val);
polymorphic_print(
ctx,

110
nac3artiq/src/lib.rs
View File

@ -23,7 +23,9 @@ use std::process::Command;
use std::rc::Rc;
use std::sync::Arc;
use inkwell::{
use itertools::Itertools;
use nac3core::codegen::{gen_func_impl, CodeGenLLVMOptions, CodeGenTargetMachineOptions};
use nac3core::inkwell::{
context::Context,
memory_buffer::MemoryBuffer,
module::{Linkage, Module},
@ -32,13 +34,10 @@ use inkwell::{
targets::*,
OptimizationLevel,
};
use itertools::Itertools;
use nac3core::codegen::irrt::setup_irrt_exceptions;
use nac3core::codegen::{gen_func_impl, CodeGenLLVMOptions, CodeGenTargetMachineOptions};
use nac3core::toplevel::builtins::get_exn_constructor;
use nac3core::typecheck::typedef::{into_var_map, TypeEnum, Unifier, VarMap};
use nac3parser::{
ast::{ExprKind, Stmt, StmtKind, StrRef},
use nac3core::nac3parser::{
ast::{Constant, ExprKind, Located, Stmt, StmtKind, StrRef},
parser::parse_program,
};
use pyo3::create_exception;
@ -195,10 +194,8 @@ impl Nac3 {
body.retain(|stmt| {
if let StmtKind::FunctionDef { ref decorator_list, .. } = stmt.node {
decorator_list.iter().any(|decorator| {
if let ExprKind::Name { id, .. } = decorator.node {
id.to_string() == "kernel"
|| id.to_string() == "portable"
|| id.to_string() == "rpc"
if let Some(id) = decorator_id_string(decorator) {
id == "kernel" || id == "portable" || id == "rpc"
} else {
false
}
@ -211,9 +208,8 @@ impl Nac3 {
}
StmtKind::FunctionDef { ref decorator_list, .. } => {
decorator_list.iter().any(|decorator| {
if let ExprKind::Name { id, .. } = decorator.node {
let id = id.to_string();
id == "extern" || id == "portable" || id == "kernel" || id == "rpc"
if let Some(id) = decorator_id_string(decorator) {
id == "extern" || id == "kernel" || id == "portable" || id == "rpc"
} else {
false
}
@ -449,7 +445,6 @@ impl Nac3 {
pyid_to_type: pyid_to_type.clone(),
primitive_ids: self.primitive_ids.clone(),
global_value_ids: global_value_ids.clone(),
class_names: Mutex::default(),
name_to_pyid: name_to_pyid.clone(),
module: module.clone(),
id_to_pyval: RwLock::default(),
@ -480,9 +475,25 @@ impl Nac3 {
match &stmt.node {
StmtKind::FunctionDef { decorator_list, .. } => {
if decorator_list.iter().any(|decorator| matches!(decorator.node, ExprKind::Name { id, .. } if id == "rpc".into())) {
store_fun.call1(py, (def_id.0.into_py(py), module.getattr(py, name.to_string().as_str()).unwrap())).unwrap();
rpc_ids.push((None, def_id));
if decorator_list
.iter()
.any(|decorator| decorator_id_string(decorator) == Some("rpc".to_string()))
{
store_fun
.call1(
py,
(
def_id.0.into_py(py),
module.getattr(py, name.to_string().as_str()).unwrap(),
),
)
.unwrap();
let is_async = decorator_list.iter().any(|decorator| {
decorator_get_flags(decorator)
.iter()
.any(|constant| *constant == Constant::Str("async".into()))
});
rpc_ids.push((None, def_id, is_async));
}
}
StmtKind::ClassDef { name, body, .. } => {
@ -490,19 +501,26 @@ impl Nac3 {
let class_obj = module.getattr(py, class_name.as_str()).unwrap();
for stmt in body {
if let StmtKind::FunctionDef { name, decorator_list, .. } = &stmt.node {
if decorator_list.iter().any(|decorator| matches!(decorator.node, ExprKind::Name { id, .. } if id == "rpc".into())) {
if decorator_list.iter().any(|decorator| {
decorator_id_string(decorator) == Some("rpc".to_string())
}) {
let is_async = decorator_list.iter().any(|decorator| {
decorator_get_flags(decorator)
.iter()
.any(|constant| *constant == Constant::Str("async".into()))
});
if name == &"__init__".into() {
return Err(CompileError::new_err(format!(
"compilation failed\n----------\nThe constructor of class {} should not be decorated with rpc decorator (at {})",
class_name, stmt.location
)));
}
rpc_ids.push((Some((class_obj.clone(), *name)), def_id));
rpc_ids.push((Some((class_obj.clone(), *name)), def_id, is_async));
}
}
}
}
_ => ()
_ => (),
}
let id = *name_to_pyid.get(&name).unwrap();
@ -541,7 +559,6 @@ impl Nac3 {
pyid_to_type: pyid_to_type.clone(),
primitive_ids: self.primitive_ids.clone(),
global_value_ids: global_value_ids.clone(),
class_names: Mutex::default(),
id_to_pyval: RwLock::default(),
id_to_primitive: RwLock::default(),
field_to_val: RwLock::default(),
@ -559,9 +576,8 @@ impl Nac3 {
.unwrap();
// Process IRRT
let context = inkwell::context::Context::create();
let irrt = load_irrt(&context);
setup_irrt_exceptions(&context, &irrt, resolver.as_ref());
let context = Context::create();
let irrt = load_irrt(&context, resolver.as_ref());
let fun_signature =
FunSignature { args: vec![], ret: self.primitive.none, vars: VarMap::new() };
@ -600,13 +616,12 @@ impl Nac3 {
let top_level = Arc::new(composer.make_top_level_context());
{
let rpc_codegen = rpc_codegen_callback();
let defs = top_level.definitions.read();
for (class_data, id) in &rpc_ids {
for (class_data, id, is_async) in &rpc_ids {
let mut def = defs[id.0].write();
match &mut *def {
TopLevelDef::Function { codegen_callback, .. } => {
*codegen_callback = Some(rpc_codegen.clone());
*codegen_callback = Some(rpc_codegen_callback(*is_async));
}
TopLevelDef::Class { methods, .. } => {
let (class_def, method_name) = class_data.as_ref().unwrap();
@ -617,7 +632,7 @@ impl Nac3 {
if let TopLevelDef::Function { codegen_callback, .. } =
&mut *defs[id.0].write()
{
*codegen_callback = Some(rpc_codegen.clone());
*codegen_callback = Some(rpc_codegen_callback(*is_async));
store_fun
.call1(
py,
@ -691,7 +706,7 @@ impl Nac3 {
let buffer = buffer.as_slice().into();
membuffer.lock().push(buffer);
})));
let size_t = Context::create()
let size_t = context
.ptr_sized_int_type(&self.get_llvm_target_machine().get_target_data(), None)
.get_bit_width();
let num_threads = if is_multithreaded() { 4 } else { 1 };
@ -710,7 +725,7 @@ impl Nac3 {
let mut generator =
ArtiqCodeGenerator::new("attributes_writeback".to_string(), size_t, self.time_fns);
let context = inkwell::context::Context::create();
let context = Context::create();
let module = context.create_module("attributes_writeback");
let target_machine = self.llvm_options.create_target_machine().unwrap();
module.set_data_layout(&target_machine.get_target_data().get_data_layout());
@ -848,6 +863,41 @@ impl Nac3 {
}
}
/// Retrieves the Name.id from a decorator, supports decorators with arguments.
fn decorator_id_string(decorator: &Located<ExprKind>) -> Option<String> {
if let ExprKind::Name { id, .. } = decorator.node {
// Bare decorator
return Some(id.to_string());
} else if let ExprKind::Call { func, .. } = &decorator.node {
// Decorators that are calls (e.g. "@rpc()") have Call for the node,
// need to extract the id from within.
if let ExprKind::Name { id, .. } = func.node {
return Some(id.to_string());
}
}
None
}
/// Retrieves flags from a decorator, if any.
fn decorator_get_flags(decorator: &Located<ExprKind>) -> Vec<Constant> {
let mut flags = vec![];
if let ExprKind::Call { keywords, .. } = &decorator.node {
for keyword in keywords {
if keyword.node.arg != Some("flags".into()) {
continue;
}
if let ExprKind::Set { elts } = &keyword.node.value.node {
for elt in elts {
if let ExprKind::Constant { value, .. } = &elt.node {
flags.push(value.clone());
}
}
}
}
}
flags
}
fn link_with_lld(elf_filename: String, obj_filename: String) -> PyResult<()> {
let linker_args = vec![
"-shared".to_string(),

185
nac3artiq/src/symbol_resolver.rs
View File

@ -1,15 +1,15 @@
use crate::PrimitivePythonId;
use inkwell::{
use itertools::Itertools;
use nac3core::inkwell::{
module::Linkage,
types::BasicType,
values::{BasicValue, BasicValueEnum},
types::{BasicType, BasicTypeEnum},
values::BasicValueEnum,
AddressSpace,
};
use itertools::Itertools;
use nac3core::nac3parser::ast::{self, StrRef};
use nac3core::{
codegen::{
model::*,
object::ndarray::{make_contiguous_strides, NDArray},
classes::{NDArrayType, ProxyType},
CodeGenContext, CodeGenerator,
},
symbol_resolver::{StaticValue, SymbolResolver, SymbolValue, ValueEnum},
@ -23,11 +23,10 @@ use nac3core::{
typedef::{into_var_map, iter_type_vars, Type, TypeEnum, TypeVar, Unifier, VarMap},
},
};
use nac3parser::ast::{self, StrRef};
use parking_lot::{Mutex, RwLock};
use parking_lot::RwLock;
use pyo3::{
types::{PyDict, PyTuple},
PyAny, PyErr, PyObject, PyResult, Python,
PyAny, PyObject, PyResult, Python,
};
use std::{
collections::{HashMap, HashSet},
@ -80,7 +79,6 @@ pub struct InnerResolver {
pub id_to_primitive: RwLock<HashMap<u64, PrimitiveValue>>,
pub field_to_val: RwLock<HashMap<ResolverField, Option<PyFieldHandle>>>,
pub global_value_ids: Arc<RwLock<HashMap<u64, PyObject>>>,
pub class_names: Mutex<HashMap<StrRef, Type>>,
pub pyid_to_def: Arc<RwLock<HashMap<u64, DefinitionId>>>,
pub pyid_to_type: Arc<RwLock<HashMap<u64, Type>>>,
pub primitive_ids: PrimitivePythonId,
@ -166,7 +164,7 @@ impl StaticValue for PythonValue {
PrimitiveValue::Bool(val) => {
ctx.ctx.i8_type().const_int(u64::from(*val), false).into()
}
PrimitiveValue::Str(val) => ctx.gen_string(generator, val).value.into(),
PrimitiveValue::Str(val) => ctx.gen_string(generator, val).into(),
});
}
if let Some(global) = ctx.module.get_global(&self.id.to_string()) {
@ -980,7 +978,7 @@ impl InnerResolver {
} else if ty_id == self.primitive_ids.string || ty_id == self.primitive_ids.np_str_ {
let val: String = obj.extract().unwrap();
self.id_to_primitive.write().insert(id, PrimitiveValue::Str(val.clone()));
Ok(Some(ctx.gen_string(generator, val).value.into()))
Ok(Some(ctx.gen_string(generator, val).into()))
} else if ty_id == self.primitive_ids.float || ty_id == self.primitive_ids.float64 {
let val: f64 = obj.extract().unwrap();
self.id_to_primitive.write().insert(id, PrimitiveValue::F64(val));
@ -1087,12 +1085,15 @@ impl InnerResolver {
let (ndarray_dtype, ndarray_ndims) =
unpack_ndarray_var_tys(&mut ctx.unifier, ndarray_ty);
let dtype = Any(ctx.get_llvm_type(generator, ndarray_dtype));
let llvm_usize = generator.get_size_type(ctx.ctx);
let ndarray_dtype_llvm_ty = ctx.get_llvm_type(generator, ndarray_dtype);
let ndarray_llvm_ty = NDArrayType::new(generator, ctx.ctx, ndarray_dtype_llvm_ty);
{
if self.global_value_ids.read().contains_key(&id) {
let global = ctx.module.get_global(&id_str).unwrap_or_else(|| {
ctx.module.add_global(
Struct(NDArray).get_type(generator, ctx.ctx),
ndarray_llvm_ty.as_underlying_type(),
Some(AddressSpace::default()),
&id_str,
)
@ -1112,138 +1113,100 @@ impl InnerResolver {
} else {
todo!("Unpacking literal of more than one element unimplemented")
};
let Ok(ndims) = u64::try_from(ndarray_ndims) else {
let Ok(ndarray_ndims) = u64::try_from(ndarray_ndims) else {
unreachable!("Expected u64 value for ndarray_ndims")
};
// Obtain the shape of the ndarray
let shape_tuple: &PyTuple = obj.getattr("shape")?.downcast()?;
assert_eq!(shape_tuple.len(), ndims as usize);
// The Rust type inferencer cannot figure this out
let shape_values: Result<Vec<Instance<'ctx, Int<SizeT>>>, PyErr> = shape_tuple
assert_eq!(shape_tuple.len(), ndarray_ndims as usize);
let shape_values: Result<Option<Vec<_>>, _> = shape_tuple
.iter()
.enumerate()
.map(|(i, elem)| {
let value = self
.get_obj_value(py, elem, ctx, generator, ctx.primitives.usize())
.map_err(|e| {
super::CompileError::new_err(format!("Error getting element {i}: {e}"))
})?
.unwrap();
let value = Int(SizeT).check_value(generator, ctx.ctx, value).unwrap();
Ok(value)
self.get_obj_value(py, elem, ctx, generator, ctx.primitives.usize()).map_err(
|e| super::CompileError::new_err(format!("Error getting element {i}: {e}")),
)
})
.collect();
let shape_values = shape_values?;
// Also use this opportunity to get the constant values of `shape_values` for calculating strides.
let shape_u64s = shape_values
.iter()
.map(|dim| {
assert!(dim.value.is_const());
dim.value.get_zero_extended_constant().unwrap()
})
.collect_vec();
let shape_values = Int(SizeT).const_array(generator, ctx.ctx, &shape_values);
let shape_values = shape_values?.unwrap();
let shape_values = llvm_usize.const_array(
&shape_values.into_iter().map(BasicValueEnum::into_int_value).collect_vec(),
);
// create a global for ndarray.shape and initialize it using the shape
let shape_global = ctx.module.add_global(
Array { len: AnyLen(ndims as u32), item: Int(SizeT) }.get_type(generator, ctx.ctx),
llvm_usize.array_type(ndarray_ndims as u32),
Some(AddressSpace::default()),
&(id_str.clone() + ".shape"),
);
shape_global.set_initializer(&shape_values.value);
shape_global.set_initializer(&shape_values);
// Obtain the (flattened) elements of the ndarray
let sz: usize = obj.getattr("size")?.extract()?;
let data_values: Vec<Instance<'ctx, Any>> = (0..sz)
let data: Result<Option<Vec<_>>, _> = (0..sz)
.map(|i| {
obj.getattr("flat")?.get_item(i).and_then(|elem| {
let value = self
.get_obj_value(py, elem, ctx, generator, ndarray_dtype)
.map_err(|e| {
super::CompileError::new_err(format!(
"Error getting element {i}: {e}"
))
})?
.unwrap();
self.get_obj_value(py, elem, ctx, generator, ndarray_dtype).map_err(|e| {
super::CompileError::new_err(format!("Error getting element {i}: {e}"))
})
})
})
.collect();
let data = data?.unwrap().into_iter();
let data = match ndarray_dtype_llvm_ty {
BasicTypeEnum::ArrayType(ty) => {
ty.const_array(&data.map(BasicValueEnum::into_array_value).collect_vec())
}
let value = dtype.check_value(generator, ctx.ctx, value).unwrap();
Ok(value)
})
})
.try_collect()?;
let data = dtype.const_array(generator, ctx.ctx, &data_values);
BasicTypeEnum::FloatType(ty) => {
ty.const_array(&data.map(BasicValueEnum::into_float_value).collect_vec())
}
BasicTypeEnum::IntType(ty) => {
ty.const_array(&data.map(BasicValueEnum::into_int_value).collect_vec())
}
BasicTypeEnum::PointerType(ty) => {
ty.const_array(&data.map(BasicValueEnum::into_pointer_value).collect_vec())
}
BasicTypeEnum::StructType(ty) => {
ty.const_array(&data.map(BasicValueEnum::into_struct_value).collect_vec())
}
BasicTypeEnum::VectorType(_) => unreachable!(),
};
// create a global for ndarray.data and initialize it using the elements
//
// NOTE: NDArray's `data` is `u8*`. Here, `data_global` is an array of `dtype`.
// We will have to cast it to an `u8*` later.
let data_global = ctx.module.add_global(
Array { len: AnyLen(sz as u32), item: dtype }.get_type(generator, ctx.ctx),
ndarray_dtype_llvm_ty.array_type(sz as u32),
Some(AddressSpace::default()),
&(id_str.clone() + ".data"),
);
data_global.set_initializer(&data.value);
// Get the constant itemsize.
let itemsize = dtype.get_type(generator, ctx.ctx).size_of().unwrap();
let itemsize = itemsize.get_zero_extended_constant().unwrap();
// Create the strides needed for ndarray.strides
let strides = make_contiguous_strides(itemsize, ndims, &shape_u64s);
let strides = strides
.into_iter()
.map(|stride| Int(SizeT).const_int(generator, ctx.ctx, stride))
.collect_vec();
let strides = Int(SizeT).const_array(generator, ctx.ctx, &strides);
// create a global for ndarray.strides and initialize it
let strides_global = ctx.module.add_global(
Array { len: AnyLen(ndims as u32), item: Int(Byte) }.get_type(generator, ctx.ctx),
Some(AddressSpace::default()),
&(id_str.clone() + ".strides"),
);
strides_global.set_initializer(&strides.value);
data_global.set_initializer(&data);
// create a global for the ndarray object and initialize it
// We are also doing [`Model::check_value`] instead of [`Model::believe_value`] to catch bugs.
let value = ndarray_llvm_ty.as_underlying_type().const_named_struct(&[
llvm_usize.const_int(ndarray_ndims, false).into(),
shape_global
.as_pointer_value()
.const_cast(llvm_usize.ptr_type(AddressSpace::default()))
.into(),
data_global
.as_pointer_value()
.const_cast(ndarray_dtype_llvm_ty.ptr_type(AddressSpace::default()))
.into(),
]);
// NOTE: data_global is an array of dtype, we want a `u8*`.
let ndarray_data = Ptr(dtype).check_value(generator, ctx.ctx, data_global).unwrap();
let ndarray_data = Ptr(Int(Byte)).pointer_cast(generator, ctx, ndarray_data.value);
let ndarray_itemsize = Int(SizeT).const_int(generator, ctx.ctx, itemsize);
let ndarray_ndims = Int(SizeT).const_int(generator, ctx.ctx, ndims);
let ndarray_shape =
Ptr(Int(SizeT)).check_value(generator, ctx.ctx, shape_global).unwrap();
let ndarray_strides =
Ptr(Int(SizeT)).check_value(generator, ctx.ctx, strides_global).unwrap();
let ndarray = Struct(NDArray).const_struct(
generator,
ctx.ctx,
&[
ndarray_data.value.as_basic_value_enum(),
ndarray_itemsize.value.as_basic_value_enum(),
ndarray_ndims.value.as_basic_value_enum(),
ndarray_shape.value.as_basic_value_enum(),
ndarray_strides.value.as_basic_value_enum(),
],
);
let ndarray_global = ctx.module.add_global(
Struct(NDArray).get_type(generator, ctx.ctx),
let ndarray = ctx.module.add_global(
ndarray_llvm_ty.as_underlying_type(),
Some(AddressSpace::default()),
&id_str,
);
ndarray_global.set_initializer(&ndarray.value);
ndarray.set_initializer(&value);
Ok(Some(ndarray_global.as_pointer_value().into()))
Ok(Some(ndarray.as_pointer_value().into()))
} else if ty_id == self.primitive_ids.tuple {
let expected_ty_enum = ctx.unifier.get_ty_immutable(expected_ty);
let TypeEnum::TTuple { ty, is_vararg_ctx: false } = expected_ty_enum.as_ref() else {

16
nac3artiq/src/timeline.rs
View File

@ -1,9 +1,9 @@
use inkwell::{
use itertools::Either;
use nac3core::codegen::CodeGenContext;
use nac3core::inkwell::{
values::{BasicValueEnum, CallSiteValue},
AddressSpace, AtomicOrdering,
};
use itertools::Either;
use nac3core::codegen::CodeGenContext;
/// Functions for manipulating the timeline.
pub trait TimeFns {
@ -31,7 +31,7 @@ impl TimeFns for NowPinningTimeFns64 {
.unwrap_or_else(|| ctx.module.add_global(i64_type, None, "now"));
let now_hiptr = ctx
.builder
.build_bitcast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
.build_bit_cast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
.map(BasicValueEnum::into_pointer_value)
.unwrap();
@ -80,7 +80,7 @@ impl TimeFns for NowPinningTimeFns64 {
.unwrap_or_else(|| ctx.module.add_global(i64_type, None, "now"));
let now_hiptr = ctx
.builder
.build_bitcast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
.build_bit_cast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
.map(BasicValueEnum::into_pointer_value)
.unwrap();
@ -109,7 +109,7 @@ impl TimeFns for NowPinningTimeFns64 {
.unwrap_or_else(|| ctx.module.add_global(i64_type, None, "now"));
let now_hiptr = ctx
.builder
.build_bitcast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
.build_bit_cast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
.map(BasicValueEnum::into_pointer_value)
.unwrap();
@ -207,7 +207,7 @@ impl TimeFns for NowPinningTimeFns {
.unwrap_or_else(|| ctx.module.add_global(i64_type, None, "now"));
let now_hiptr = ctx
.builder
.build_bitcast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
.build_bit_cast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
.map(BasicValueEnum::into_pointer_value)
.unwrap();
@ -258,7 +258,7 @@ impl TimeFns for NowPinningTimeFns {
let time_lo = ctx.builder.build_int_truncate(time, i32_type, "time.lo").unwrap();
let now_hiptr = ctx
.builder
.build_bitcast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
.build_bit_cast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
.map(BasicValueEnum::into_pointer_value)
.unwrap();

4
nac3core/Cargo.toml
View File

@ -18,9 +18,9 @@ strum = "0.26"
strum_macros = "0.26"
[dependencies.inkwell]
version = "0.4"
version = "0.5"
default-features = false
features = ["llvm14-0", "target-x86", "target-arm", "target-riscv", "no-libffi-linking"]
features = ["llvm14-0-prefer-dynamic", "target-x86", "target-arm", "target-riscv", "no-libffi-linking"]
[dev-dependencies]
test-case = "1.2.0"

4
nac3core/build.rs
View File

@ -8,7 +8,6 @@ use std::{
};
fn main() {
// Define relevant directories
let out_dir = env::var("OUT_DIR").unwrap();
let out_dir = Path::new(&out_dir);
let irrt_dir = Path::new("irrt");
@ -23,6 +22,7 @@ fn main() {
"--target=wasm32",
"-x",
"c++",
"-std=c++20",
"-fno-discard-value-names",
"-fno-exceptions",
"-fno-rtti",
@ -98,8 +98,6 @@ fn main() {
file.write_all(filtered_output.as_bytes()).unwrap();
}
// Assemble the emitted and filtered IR to .bc
// That .bc will be integrated into nac3core's codegen
let mut llvm_as = Command::new("llvm-as-irrt")
.stdin(Stdio::piped())
.arg("-o")

22
nac3core/irrt/irrt.cpp
View File

@ -1,16 +1,6 @@
#include <irrt/exception.hpp>
#include <irrt/int_types.hpp>
#include <irrt/list.hpp>
#include <irrt/math_util.hpp>
#include <irrt/ndarray/array.hpp>
#include <irrt/ndarray/basic.hpp>
#include <irrt/ndarray/broadcast.hpp>
#include <irrt/ndarray/def.hpp>
#include <irrt/ndarray/indexing.hpp>
#include <irrt/ndarray/iter.hpp>
#include <irrt/ndarray/matmul.hpp>
#include <irrt/ndarray/reshape.hpp>
#include <irrt/ndarray/transpose.hpp>
#include <irrt/original.hpp>
#include <irrt/range.hpp>
#include <irrt/slice.hpp>
#include "irrt/exception.hpp"
#include "irrt/int_types.hpp"
#include "irrt/list.hpp"
#include "irrt/math.hpp"
#include "irrt/ndarray.hpp"
#include "irrt/slice.hpp"

6
nac3core/irrt/irrt/cslice.hpp
View File

@ -1,9 +1,9 @@
#pragma once
#include <irrt/int_types.hpp>
#include "irrt/int_types.hpp"
template <typename SizeT> struct CSlice
{
template<typename SizeT>
struct CSlice {
uint8_t* base;
SizeT len;
};

20
nac3core/irrt/irrt/cstr_util.hpp
View File

@ -1,20 +0,0 @@
#pragma once
#include <irrt/int_types.hpp>
namespace cstr
{
/**
* @brief Implementation of `strlen()`.
*/
uint32_t length(const char *str)
{
uint32_t length = 0;
while (*str != '\0')
{
length++;
str++;
}
return length;
}
} // namespace cstr

13
nac3core/irrt/irrt/debug.hpp
View File

@ -1,5 +1,6 @@
#pragma once
// Set in nac3core/build.rs
#ifdef IRRT_DEBUG_ASSERT
#define IRRT_DEBUG_ASSERT_BOOL true
#else
@ -7,16 +8,18 @@
#endif
#define raise_debug_assert(SizeT, msg, param1, param2, param3) \
raise_exception(SizeT, EXN_ASSERTION_ERROR, "IRRT debug assert failed: " msg, param1, param2, param3);
raise_exception(SizeT, EXN_ASSERTION_ERROR, "IRRT debug assert failed: " msg, param1, param2, param3)
#define debug_assert_eq(SizeT, lhs, rhs) \
if (IRRT_DEBUG_ASSERT_BOOL && (lhs) != (rhs)) \
{ \
if constexpr (IRRT_DEBUG_ASSERT_BOOL) { \
if ((lhs) != (rhs)) { \
raise_debug_assert(SizeT, "LHS = {0}. RHS = {1}", lhs, rhs, NO_PARAM); \
} \
}
#define debug_assert(SizeT, expr) \
if (IRRT_DEBUG_ASSERT_BOOL && !(expr)) \
{ \
if constexpr (IRRT_DEBUG_ASSERT_BOOL) { \
if (!(expr)) { \
raise_debug_assert(SizeT, "Got false.", NO_PARAM, NO_PARAM, NO_PARAM); \
} \
}

38
nac3core/irrt/irrt/exception.hpp
View File

@ -1,8 +1,7 @@
#pragma once
#include <irrt/cslice.hpp>
#include <irrt/cstr_util.hpp>
#include <irrt/int_types.hpp>
#include "irrt/cslice.hpp"
#include "irrt/int_types.hpp"
/**
* @brief The int type of ARTIQ exception IDs.
@ -13,8 +12,7 @@ typedef int32_t ExceptionId;
* Set of exceptions C++ IRRT can use.
* Must be synchronized with `setup_irrt_exceptions` in `nac3core/src/codegen/irrt/mod.rs`.
*/
extern "C"
{
extern "C" {
ExceptionId EXN_INDEX_ERROR;
ExceptionId EXN_VALUE_ERROR;
ExceptionId EXN_ASSERTION_ERROR;
@ -29,13 +27,12 @@ extern "C"
*/
extern "C" void __nac3_raise(void* err);
namespace
{
namespace {
/**
* @brief NAC3's Exception struct
*/
template <typename SizeT> struct Exception
{
template<typename SizeT>
struct Exception {
ExceptionId id;
CSlice<SizeT> filename;
int32_t line;
@ -45,24 +42,29 @@ template <typename SizeT> struct Exception
int64_t params[3];
};
const int64_t NO_PARAM = 0;
constexpr int64_t NO_PARAM = 0;
template<typename SizeT>
void _raise_exception_helper(ExceptionId id, const char *filename, int32_t line, const char *function, const char *msg,
int64_t param0, int64_t param1, int64_t param2)
{
void _raise_exception_helper(ExceptionId id,
const char* filename,
int32_t line,
const char* function,
const char* msg,
int64_t param0,
int64_t param1,
int64_t param2) {
Exception<SizeT> e = {
.id = id,
.filename = {.base = (uint8_t *)filename, .len = (int32_t)cstr::length(filename)},
.filename = {.base = reinterpret_cast<const uint8_t*>(filename), .len = __builtin_strlen(filename)},
.line = line,
.column = 0,
.function = {.base = (uint8_t *)function, .len = (int32_t)cstr::length(function)},
.msg = {.base = (uint8_t *)msg, .len = (int32_t)cstr::length(msg)},
.function = {.base = reinterpret_cast<const uint8_t*>(function), .len = __builtin_strlen(function)},
.msg = {.base = reinterpret_cast<const uint8_t*>(msg), .len = __builtin_strlen(msg)},
};
e.params[0] = param0;
e.params[1] = param1;
e.params[2] = param2;
__nac3_raise((void *)&e);
__nac3_raise(reinterpret_cast<void*>(&e));
__builtin_unreachable();
}
@ -72,7 +74,7 @@ void _raise_exception_helper(ExceptionId id, const char *filename, int32_t line,
* @param id The ID of the exception to raise.
* @param msg A global constant C-string of the error message.
*
* `param0` and `param2` are optional format arguments of `msg`. They should be set to
* `param0` to `param2` are optional format arguments of `msg`. They should be set to
* `NO_PARAM` to indicate they are unused.
*/
#define raise_exception(SizeT, id, msg, param0, param1, param2) \

14
nac3core/irrt/irrt/int_types.hpp
View File

@ -1,8 +1,22 @@
#pragma once
#if __STDC_VERSION__ >= 202000
using int8_t = _BitInt(8);
using uint8_t = unsigned _BitInt(8);
using int32_t = _BitInt(32);
using uint32_t = unsigned _BitInt(32);
using int64_t = _BitInt(64);
using uint64_t = unsigned _BitInt(64);
#else
using int8_t = _ExtInt(8);
using uint8_t = unsigned _ExtInt(8);
using int32_t = _ExtInt(32);
using uint32_t = unsigned _ExtInt(32);
using int64_t = _ExtInt(64);
using uint64_t = unsigned _ExtInt(64);
#endif
// NDArray indices are always `uint32_t`.
using NDIndex = uint32_t;
// The type of an index or a value describing the length of a range/slice is always `int32_t`.
using SliceIndex = int32_t;

122
nac3core/irrt/irrt/list.hpp
View File

@ -1,59 +1,75 @@
#pragma once
#include <irrt/debug.hpp>
#include <irrt/exception.hpp>
#include <irrt/int_types.hpp>
#include <irrt/slice.hpp>
#include "irrt/int_types.hpp"
#include "irrt/math_util.hpp"
namespace
{
/**
* @brief A list in NAC3.
*
* The `items` field is opaque. You must rely on external contexts to
* know how to interpret it.
*/
template <typename SizeT> struct List
{
uint8_t *items;
SizeT len;
};
namespace list
{
template <typename SizeT> void range_assign(List<SizeT> *dst, SizeT itemsize, Range<SizeT> *range, List<SizeT> *src)
{
debug_assert(range->step != 0);
SizeT assign_len = range->len();
if (assign_len < src->len)
{
// Encountered things like
// ```
// xs = [1, 2, 3, 4, 5]
// xs[1:3] = [999, 1000, 1001, 1002] # Note that step has to be 1.
// xs = [1, 999, 1000, 1001, 1002, 4, 5] # xs is longer
// ```
//
// We do not support extending lists since that requires allocation.
raise_exception(SizeT, EXN_VALUE_ERROR,
"List assignment does not support list extension. Attempting to assign {0} item(s) into a "
"space of {1} item(s).",
src->len, assign_len, NO_PARAM);
extern "C" {
// Handle list assignment and dropping part of the list when
// both dest_step and src_step are +1.
// - All the index must *not* be out-of-bound or negative,
// - The end index is *inclusive*,
// - The length of src and dest slice size should already
// be checked: if dest.step == 1 then len(src) <= len(dest) else len(src) == len(dest)
SliceIndex __nac3_list_slice_assign_var_size(SliceIndex dest_start,
SliceIndex dest_end,
SliceIndex dest_step,
uint8_t* dest_arr,
SliceIndex dest_arr_len,
SliceIndex src_start,
SliceIndex src_end,
SliceIndex src_step,
uint8_t* src_arr,
SliceIndex src_arr_len,
const SliceIndex size) {
/* if dest_arr_len == 0, do nothing since we do not support extending list */
if (dest_arr_len == 0)
return dest_arr_len;
/* if both step is 1, memmove directly, handle the dropping of the list, and shrink size */
if (src_step == dest_step && dest_step == 1) {
const SliceIndex src_len = (src_end >= src_start) ? (src_end - src_start + 1) : 0;
const SliceIndex dest_len = (dest_end >= dest_start) ? (dest_end - dest_start + 1) : 0;
if (src_len > 0) {
__builtin_memmove(dest_arr + dest_start * size, src_arr + src_start * size, src_len * size);
}
if (range->step == 1)
{
// Assigning into a contiguous region. Optimized with memmove.
uint8_t* p1 = dst->items + range->start * itemsize;
uint8_t* p2 = dst->items + range->start * itemsize + assign_len * itemsize;
__builtin_memmove(cursor, src->items, assign_len * itemsize);
cursor += range_len * itemsize;
__builtin_memmove(cursor, );
if (dest_len > 0) {
/* dropping */
__builtin_memmove(dest_arr + (dest_start + src_len) * size, dest_arr + (dest_end + 1) * size,
(dest_arr_len - dest_end - 1) * size);
}
/* shrink size */
return dest_arr_len - (dest_len - src_len);
}
/* if two range overlaps, need alloca */
uint8_t need_alloca = (dest_arr == src_arr)
&& !(max(dest_start, dest_end) < min(src_start, src_end)
|| max(src_start, src_end) < min(dest_start, dest_end));
if (need_alloca) {
uint8_t* tmp = reinterpret_cast<uint8_t*>(__builtin_alloca(src_arr_len * size));
__builtin_memcpy(tmp, src_arr, src_arr_len * size);
src_arr = tmp;
}
SliceIndex src_ind = src_start;
SliceIndex dest_ind = dest_start;
for (; (src_step > 0) ? (src_ind <= src_end) : (src_ind >= src_end); src_ind += src_step, dest_ind += dest_step) {
/* for constant optimization */
if (size == 1) {
__builtin_memcpy(dest_arr + dest_ind, src_arr + src_ind, 1);
} else if (size == 4) {
__builtin_memcpy(dest_arr + dest_ind * 4, src_arr + src_ind * 4, 4);
} else if (size == 8) {
__builtin_memcpy(dest_arr + dest_ind * 8, src_arr + src_ind * 8, 8);
} else {
/* memcpy for var size, cannot overlap after previous alloca */
__builtin_memcpy(dest_arr + dest_ind * size, src_arr + src_ind * size, size);
}
}
} // namespace list
} // namespace
/* only dest_step == 1 can we shrink the dest list. */
/* size should be ensured prior to calling this function */
if (dest_step == 1 && dest_end >= dest_start) {
__builtin_memmove(dest_arr + dest_ind * size, dest_arr + (dest_end + 1) * size,
(dest_arr_len - dest_end - 1) * size);
return dest_arr_len - (dest_end - dest_ind) - 1;
}
return dest_arr_len;
}
} // extern "C"

93
nac3core/irrt/irrt/math.hpp Normal file
View File

@ -0,0 +1,93 @@
#pragma once
namespace {
// adapted from GNU Scientific Library: https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
// need to make sure `exp >= 0` before calling this function
template<typename T>
T __nac3_int_exp_impl(T base, T exp) {
T res = 1;
/* repeated squaring method */
do {
if (exp & 1) {
res *= base; /* for n odd */
}
exp >>= 1;
base *= base;
} while (exp);
return res;
}
} // namespace
#define DEF_nac3_int_exp_(T) \
T __nac3_int_exp_##T(T base, T exp) { \
return __nac3_int_exp_impl(base, exp); \
}
extern "C" {
// Putting semicolons here to make clang-format not reformat this into
// a stair shape.
DEF_nac3_int_exp_(int32_t);
DEF_nac3_int_exp_(int64_t);
DEF_nac3_int_exp_(uint32_t);
DEF_nac3_int_exp_(uint64_t);
int32_t __nac3_isinf(double x) {
return __builtin_isinf(x);
}
int32_t __nac3_isnan(double x) {
return __builtin_isnan(x);
}
double tgamma(double arg);
double __nac3_gamma(double z) {
// Handling for denormals
// | x | Python gamma(x) | C tgamma(x) |
// --- | ----------------- | --------------- | ----------- |
// (1) | nan | nan | nan |
// (2) | -inf | -inf | inf |
// (3) | inf | inf | inf |
// (4) | 0.0 | inf | inf |
// (5) | {-1.0, -2.0, ...} | inf | nan |
// (1)-(3)
if (__builtin_isinf(z) || __builtin_isnan(z)) {
return z;
}
double v = tgamma(z);
// (4)-(5)
return __builtin_isinf(v) || __builtin_isnan(v) ? __builtin_inf() : v;
}
double lgamma(double arg);
double __nac3_gammaln(double x) {
// libm's handling of value overflows differs from scipy:
// - scipy: gammaln(-inf) -> -inf
// - libm : lgamma(-inf) -> inf
if (__builtin_isinf(x)) {
return x;
}
return lgamma(x);
}
double j0(double x);
double __nac3_j0(double x) {
// libm's handling of value overflows differs from scipy:
// - scipy: j0(inf) -> nan
// - libm : j0(inf) -> 0.0
if (__builtin_isinf(x)) {
return __builtin_nan("");
}
return j0(x);
}
}

11
nac3core/irrt/irrt/math_util.hpp
View File

@ -1,14 +1,13 @@
#pragma once
namespace
{
template <typename T> const T &max(const T &a, const T &b)
{
namespace {
template<typename T>
const T& max(const T& a, const T& b) {
return a > b ? a : b;
}
template <typename T> const T &min(const T &a, const T &b)
{
template<typename T>
const T& min(const T& a, const T& b) {
return a > b ? b : a;
}
} // namespace

144
nac3core/irrt/irrt/ndarray.hpp Normal file
View File

@ -0,0 +1,144 @@
#pragma once
#include "irrt/int_types.hpp"
namespace {
template<typename SizeT>
SizeT __nac3_ndarray_calc_size_impl(const SizeT* list_data, SizeT list_len, SizeT begin_idx, SizeT end_idx) {
__builtin_assume(end_idx <= list_len);
SizeT num_elems = 1;
for (SizeT i = begin_idx; i < end_idx; ++i) {
SizeT val = list_data[i];
__builtin_assume(val > 0);
num_elems *= val;
}
return num_elems;
}
template<typename SizeT>
void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims, SizeT num_dims, NDIndex* idxs) {
SizeT stride = 1;
for (SizeT dim = 0; dim < num_dims; dim++) {
SizeT i = num_dims - dim - 1;
__builtin_assume(dims[i] > 0);
idxs[i] = (index / stride) % dims[i];
stride *= dims[i];
}
}
template<typename SizeT>
SizeT __nac3_ndarray_flatten_index_impl(const SizeT* dims, SizeT num_dims, const NDIndex* indices, SizeT num_indices) {
SizeT idx = 0;
SizeT stride = 1;
for (SizeT i = 0; i < num_dims; ++i) {
SizeT ri = num_dims - i - 1;
if (ri < num_indices) {
idx += stride * indices[ri];
}
__builtin_assume(dims[i] > 0);
stride *= dims[ri];
}
return idx;
}
template<typename SizeT>
void __nac3_ndarray_calc_broadcast_impl(const SizeT* lhs_dims,
SizeT lhs_ndims,
const SizeT* rhs_dims,
SizeT rhs_ndims,
SizeT* out_dims) {
SizeT max_ndims = lhs_ndims > rhs_ndims ? lhs_ndims : rhs_ndims;
for (SizeT i = 0; i < max_ndims; ++i) {
const SizeT* lhs_dim_sz = i < lhs_ndims ? &lhs_dims[lhs_ndims - i - 1] : nullptr;
const SizeT* rhs_dim_sz = i < rhs_ndims ? &rhs_dims[rhs_ndims - i - 1] : nullptr;
SizeT* out_dim = &out_dims[max_ndims - i - 1];
if (lhs_dim_sz == nullptr) {
*out_dim = *rhs_dim_sz;
} else if (rhs_dim_sz == nullptr) {
*out_dim = *lhs_dim_sz;
} else if (*lhs_dim_sz == 1) {
*out_dim = *rhs_dim_sz;
} else if (*rhs_dim_sz == 1) {
*out_dim = *lhs_dim_sz;
} else if (*lhs_dim_sz == *rhs_dim_sz) {
*out_dim = *lhs_dim_sz;
} else {
__builtin_unreachable();
}
}
}
template<typename SizeT>
void __nac3_ndarray_calc_broadcast_idx_impl(const SizeT* src_dims,
SizeT src_ndims,
const NDIndex* in_idx,
NDIndex* out_idx) {
for (SizeT i = 0; i < src_ndims; ++i) {
SizeT src_i = src_ndims - i - 1;
out_idx[src_i] = src_dims[src_i] == 1 ? 0 : in_idx[src_i];
}
}
} // namespace
extern "C" {
uint32_t __nac3_ndarray_calc_size(const uint32_t* list_data, uint32_t list_len, uint32_t begin_idx, uint32_t end_idx) {
return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx, end_idx);
}
uint64_t
__nac3_ndarray_calc_size64(const uint64_t* list_data, uint64_t list_len, uint64_t begin_idx, uint64_t end_idx) {
return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx, end_idx);
}
void __nac3_ndarray_calc_nd_indices(uint32_t index, const uint32_t* dims, uint32_t num_dims, NDIndex* idxs) {
__nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
}
void __nac3_ndarray_calc_nd_indices64(uint64_t index, const uint64_t* dims, uint64_t num_dims, NDIndex* idxs) {
__nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
}
uint32_t
__nac3_ndarray_flatten_index(const uint32_t* dims, uint32_t num_dims, const NDIndex* indices, uint32_t num_indices) {
return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices, num_indices);
}
uint64_t
__nac3_ndarray_flatten_index64(const uint64_t* dims, uint64_t num_dims, const NDIndex* indices, uint64_t num_indices) {
return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices, num_indices);
}
void __nac3_ndarray_calc_broadcast(const uint32_t* lhs_dims,
uint32_t lhs_ndims,
const uint32_t* rhs_dims,
uint32_t rhs_ndims,
uint32_t* out_dims) {
return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims, rhs_ndims, out_dims);
}
void __nac3_ndarray_calc_broadcast64(const uint64_t* lhs_dims,
uint64_t lhs_ndims,
const uint64_t* rhs_dims,
uint64_t rhs_ndims,
uint64_t* out_dims) {
return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims, rhs_ndims, out_dims);
}
void __nac3_ndarray_calc_broadcast_idx(const uint32_t* src_dims,
uint32_t src_ndims,
const NDIndex* in_idx,
NDIndex* out_idx) {
__nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx, out_idx);
}
void __nac3_ndarray_calc_broadcast_idx64(const uint64_t* src_dims,
uint64_t src_ndims,
const NDIndex* in_idx,
NDIndex* out_idx) {
__nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx, out_idx);
}
}

130
nac3core/irrt/irrt/ndarray/array.hpp
View File

@ -1,130 +0,0 @@
#pragma once
#include <irrt/debug.hpp>
#include <irrt/exception.hpp>
#include <irrt/int_types.hpp>
#include <irrt/list.hpp>
#include <irrt/ndarray/basic.hpp>
#include <irrt/ndarray/def.hpp>
namespace
{
namespace ndarray
{
namespace array
{
template <typename SizeT>
void set_and_validate_list_shape_helper(SizeT axis, List<SizeT> *list, SizeT ndims, SizeT *shape)
{
if (shape[axis] == -1)
{
// Dimension is unspecified. Set it.
shape[axis] = list->len;
}
else
{
// Dimension is specified. Check.
if (shape[axis] != list->len)
{
// Mismatch, throw an error.
// NOTE: NumPy's error message is more complex and needs more PARAMS to display.
raise_exception(SizeT, EXN_VALUE_ERROR,
"The requested array has an inhomogenous shape "
"after {0} dimension(s).",
axis, shape[axis], list->len);
}
}
if (axis + 1 == ndims)
{
// `list` has type `list[ItemType]`
// Do nothing
}
else
{
// `list` has type `list[list[...]]`
List<SizeT> **lists = (List<SizeT> **)(list->items);
for (SizeT i = 0; i < list->len; i++)
{
set_and_validate_list_shape_helper<SizeT>(axis + 1, lists[i], ndims, shape);
}
}
}
// TODO: Document me
template <typename SizeT> void set_and_validate_list_shape(List<SizeT> *list, SizeT ndims, SizeT *shape)
{
for (SizeT axis = 0; axis < ndims; axis++)
{
shape[axis] = -1; // Sentinel to say this dimension is unspecified.
}
set_and_validate_list_shape_helper<SizeT>(0, list, ndims, shape);
}
template <typename SizeT>
void write_list_to_array_helper(SizeT axis, SizeT *index, List<SizeT> *list, NDArray<SizeT> *ndarray)
{
debug_assert_eq(SizeT, list->len, ndarray->shape[axis]);
if (IRRT_DEBUG_ASSERT_BOOL)
{
if (!ndarray::basic::is_c_contiguous(ndarray))
{
raise_debug_assert(SizeT, "ndarray is not C-contiguous", ndarray->strides[0], ndarray->strides[1],
NO_PARAM);
}
}
if (axis + 1 == ndarray->ndims)
{
// `list` has type `list[ItemType]`
// `ndarray` is contiguous, so we can do this, and this is fast.
uint8_t *dst = ndarray->data + (ndarray->itemsize * (*index));
__builtin_memcpy(dst, list->items, ndarray->itemsize * list->len);
*index += list->len;
}
else
{
// `list` has type `list[list[...]]`
List<SizeT> **lists = (List<SizeT> **)(list->items);
for (SizeT i = 0; i < list->len; i++)
{
write_list_to_array_helper<SizeT>(axis + 1, index, lists[i], ndarray);
}
}
}
// TODO: Document me
template <typename SizeT> void write_list_to_array(List<SizeT> *list, NDArray<SizeT> *ndarray)
{
SizeT index = 0;
write_list_to_array_helper<SizeT>((SizeT)0, &index, list, ndarray);
}
} // namespace array
} // namespace ndarray
} // namespace
extern "C"
{
using namespace ndarray::array;
void __nac3_ndarray_array_set_and_validate_list_shape(List<int32_t> *list, int32_t ndims, int32_t *shape)
{
set_and_validate_list_shape(list, ndims, shape);
}
void __nac3_ndarray_array_set_and_validate_list_shape64(List<int64_t> *list, int64_t ndims, int64_t *shape)
{
set_and_validate_list_shape(list, ndims, shape);
}
void __nac3_ndarray_array_write_list_to_array(List<int32_t> *list, NDArray<int32_t> *ndarray)
{
write_list_to_array(list, ndarray);
}
void __nac3_ndarray_array_write_list_to_array64(List<int64_t> *list, NDArray<int64_t> *ndarray)
{
write_list_to_array(list, ndarray);
}
}

380
nac3core/irrt/irrt/ndarray/basic.hpp
View File

@ -1,380 +0,0 @@
#pragma once
#include <irrt/debug.hpp>
#include <irrt/exception.hpp>
#include <irrt/int_types.hpp>
#include <irrt/ndarray/def.hpp>
namespace
{
namespace ndarray
{
namespace basic
{
/**
* @brief Asserts that `shape` does not contain negative dimensions.
*
* @param ndims Number of dimensions in `shape`
* @param shape The shape to check on
*/
template <typename SizeT> void assert_shape_no_negative(SizeT ndims, const SizeT *shape)
{
for (SizeT axis = 0; axis < ndims; axis++)
{
if (shape[axis] < 0)
{
raise_exception(SizeT, EXN_VALUE_ERROR,
"negative dimensions are not allowed; axis {0} "
"has dimension {1}",
axis, shape[axis], NO_PARAM);
}
}
}
/**
* @brief Check two shapes are the same in the context of writing outputting to an ndarray.
*
* This function throws error messages for output shape mismatches.
*/
template <typename SizeT>
void assert_output_shape_same(SizeT ndarray_ndims, const SizeT *ndarray_shape, SizeT output_ndims,
const SizeT *output_shape)
{
if (ndarray_ndims != output_ndims)
{
// There is no corresponding NumPy error message like this.
raise_exception(SizeT, EXN_VALUE_ERROR, "Cannot write output of ndims {0} to an ndarray with ndims {1}",
output_ndims, ndarray_ndims, NO_PARAM);
}
for (SizeT axis = 0; axis < ndarray_ndims; axis++)
{
if (ndarray_shape[axis] != output_shape[axis])
{
// There is no corresponding NumPy error message like this.
raise_exception(SizeT, EXN_VALUE_ERROR,
"Mismatched dimensions on axis {0}, output has "
"dimension {1}, but destination ndarray has dimension {2}.",
axis, output_shape[axis], ndarray_shape[axis]);
}
}
}
/**
* @brief Returns the number of elements of an ndarray given its shape.
*
* @param ndims Number of dimensions in `shape`
* @param shape The shape of the ndarray
*/
template <typename SizeT> SizeT calc_size_from_shape(SizeT ndims, const SizeT *shape)
{
SizeT size = 1;
for (SizeT axis = 0; axis < ndims; axis++)
size *= shape[axis];
return size;
}
/**
* @brief Compute the array indices of the `nth` (0-based) element of an ndarray given only its shape.
*
* @param ndims Number of elements in `shape` and `indices`
* @param shape The shape of the ndarray
* @param indices The returned indices indexing the ndarray with shape `shape`.
* @param nth The index of the element of interest.
*/
template <typename SizeT> void set_indices_by_nth(SizeT ndims, const SizeT *shape, SizeT *indices, SizeT nth)
{
for (SizeT i = 0; i < ndims; i++)
{
SizeT axis = ndims - i - 1;
SizeT dim = shape[axis];
indices[axis] = nth % dim;
nth /= dim;
}
}
/**
* @brief Return the number of elements of an `ndarray`
*
* This function corresponds to `<an_ndarray>.size`
*/
template <typename SizeT> SizeT size(const NDArray<SizeT> *ndarray)
{
return calc_size_from_shape(ndarray->ndims, ndarray->shape);
}
/**
* @brief Return of the number of its content of an `ndarray`.
*
* This function corresponds to `<an_ndarray>.nbytes`.
*/
template <typename SizeT> SizeT nbytes(const NDArray<SizeT> *ndarray)
{
return size(ndarray) * ndarray->itemsize;
}
/**
* @brief Get the `len()` of an ndarray, and asserts that `ndarray` is a sized object.
*
* This function corresponds to `<an_ndarray>.__len__`.
*
* @param dst_length The returned result
*/
template <typename SizeT> SizeT len(const NDArray<SizeT> *ndarray)
{
// numpy prohibits `__len__` on unsized objects
if (ndarray->ndims == 0)
{
raise_exception(SizeT, EXN_TYPE_ERROR, "len() of unsized object", NO_PARAM, NO_PARAM, NO_PARAM);
}
else
{
return ndarray->shape[0];
}
}
/**
* @brief Return a boolean indicating if `ndarray` is (C-)contiguous.
*
* You may want to see: ndarray's rules for C-contiguity: https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45
*/
template <typename SizeT> bool is_c_contiguous(const NDArray<SizeT> *ndarray)
{
// Other references:
// - tinynumpy's implementation: https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L102
// - ndarray's flags["C_CONTIGUOUS"]: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags
// - ndarray's rules for C-contiguity: https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45
// From https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45:
//
// The traditional rule is that for an array to be flagged as C contiguous,
// the following must hold:
//
// strides[-1] == itemsize
// strides[i] == shape[i+1] * strides[i + 1]
// [...]
// According to these rules, a 0- or 1-dimensional array is either both
// C- and F-contiguous, or neither; and an array with 2+ dimensions
// can be C- or F- contiguous, or neither, but not both. Though there
// there are exceptions for arrays with zero or one item, in the first
// case the check is relaxed up to and including the first dimension
// with shape[i] == 0. In the second case `strides == itemsize` will
// can be true for all dimensions and both flags are set.
if (ndarray->ndims == 0)
{
return true;
}
if (ndarray->strides[ndarray->ndims - 1] != ndarray->itemsize)
{
return false;
}
for (SizeT i = 1; i < ndarray->ndims; i++)
{
SizeT axis_i = ndarray->ndims - i - 1;
if (ndarray->strides[axis_i] != ndarray->shape[axis_i + 1] * ndarray->strides[axis_i + 1])
{
return false;
}
}
return true;
}
/**
* @brief Return the pointer to the element indexed by `indices`.
*/
template <typename SizeT> uint8_t *get_pelement_by_indices(const NDArray<SizeT> *ndarray, const SizeT *indices)
{
uint8_t *element = ndarray->data;
for (SizeT dim_i = 0; dim_i < ndarray->ndims; dim_i++)
element += indices[dim_i] * ndarray->strides[dim_i];
return element;
}
/**
* @brief Convenience function. Like `get_pelement_by_indices` but
* reinterprets the element pointer.
*/
template <typename SizeT, typename T> T *get_ptr(const NDArray<SizeT> *ndarray, const SizeT *indices)
{
return (T *)get_pelement_by_indices(ndarray, indices);
}
/**
* @brief Return the pointer to the nth (0-based) element in a flattened view of `ndarray`.
*
* This function does no bound check.
*/
template <typename SizeT> uint8_t *get_nth_pelement(const NDArray<SizeT> *ndarray, SizeT nth)
{
uint8_t *element = ndarray->data;
for (SizeT i = 0; i < ndarray->ndims; i++)
{
SizeT axis = ndarray->ndims - i - 1;
SizeT dim = ndarray->shape[axis];
element += ndarray->strides[axis] * (nth % dim);
nth /= dim;
}
return element;
}
/**
* @brief Update the strides of an ndarray given an ndarray `shape`
* and assuming that the ndarray is fully c-contagious.
*
* You might want to read https://ajcr.net/stride-guide-part-1/.
*/
template <typename SizeT> void set_strides_by_shape(NDArray<SizeT> *ndarray)
{
SizeT stride_product = 1;
for (SizeT i = 0; i < ndarray->ndims; i++)
{
SizeT axis = ndarray->ndims - i - 1;
ndarray->strides[axis] = stride_product * ndarray->itemsize;
stride_product *= ndarray->shape[axis];
}
}
/**
* @brief Set an element in `ndarray`.
*
* @param pelement Pointer to the element in `ndarray` to be set.
* @param pvalue Pointer to the value `pelement` will be set to.
*/
template <typename SizeT> void set_pelement_value(NDArray<SizeT> *ndarray, uint8_t *pelement, const uint8_t *pvalue)
{
__builtin_memcpy(pelement, pvalue, ndarray->itemsize);
}
/**
* @brief Copy data from one ndarray to another of the exact same size and itemsize.
*
* Both ndarrays will be viewed in their flatten views when copying the elements.
*/
template <typename SizeT> void copy_data(const NDArray<SizeT> *src_ndarray, NDArray<SizeT> *dst_ndarray)
{
// TODO: Make this faster with memcpy
debug_assert_eq(SizeT, src_ndarray->itemsize, dst_ndarray->itemsize);
for (SizeT i = 0; i < size(src_ndarray); i++)
{
auto src_element = ndarray::basic::get_nth_pelement(src_ndarray, i);
auto dst_element = ndarray::basic::get_nth_pelement(dst_ndarray, i);
ndarray::basic::set_pelement_value(dst_ndarray, dst_element, src_element);
}
}
} // namespace basic
} // namespace ndarray
} // namespace
extern "C"
{
using namespace ndarray::basic;
void __nac3_ndarray_util_assert_shape_no_negative(int32_t ndims, int32_t *shape)
{
assert_shape_no_negative(ndims, shape);
}
void __nac3_ndarray_util_assert_shape_no_negative64(int64_t ndims, int64_t *shape)
{
assert_shape_no_negative(ndims, shape);
}
void __nac3_ndarray_util_assert_output_shape_same(int32_t ndarray_ndims, const int32_t *ndarray_shape,
int32_t output_ndims, const int32_t *output_shape)
{
assert_output_shape_same(ndarray_ndims, ndarray_shape, output_ndims, output_shape);
}
void __nac3_ndarray_util_assert_output_shape_same64(int64_t ndarray_ndims, const int64_t *ndarray_shape,
int64_t output_ndims, const int64_t *output_shape)
{
assert_output_shape_same(ndarray_ndims, ndarray_shape, output_ndims, output_shape);
}
uint32_t __nac3_ndarray_size(NDArray<int32_t> *ndarray)
{
return size(ndarray);
}
uint64_t __nac3_ndarray_size64(NDArray<int64_t> *ndarray)
{
return size(ndarray);
}
uint32_t __nac3_ndarray_nbytes(NDArray<int32_t> *ndarray)
{
return nbytes(ndarray);
}
uint64_t __nac3_ndarray_nbytes64(NDArray<int64_t> *ndarray)
{
return nbytes(ndarray);
}
int32_t __nac3_ndarray_len(NDArray<int32_t> *ndarray)
{
return len(ndarray);
}
int64_t __nac3_ndarray_len64(NDArray<int64_t> *ndarray)
{
return len(ndarray);
}
bool __nac3_ndarray_is_c_contiguous(NDArray<int32_t> *ndarray)
{
return is_c_contiguous(ndarray);
}
bool __nac3_ndarray_is_c_contiguous64(NDArray<int64_t> *ndarray)
{
return is_c_contiguous(ndarray);
}
uint8_t *__nac3_ndarray_get_nth_pelement(const NDArray<int32_t> *ndarray, int32_t nth)
{
return get_nth_pelement(ndarray, nth);
}
uint8_t *__nac3_ndarray_get_nth_pelement64(const NDArray<int64_t> *ndarray, int64_t nth)
{
return get_nth_pelement(ndarray, nth);
}
uint8_t *__nac3_ndarray_get_pelement_by_indices(const NDArray<int32_t> *ndarray, int32_t *indices)
{
return get_pelement_by_indices(ndarray, indices);
}
uint8_t *__nac3_ndarray_get_pelement_by_indices64(const NDArray<int64_t> *ndarray, int64_t *indices)
{
return get_pelement_by_indices(ndarray, indices);
}
void __nac3_ndarray_set_strides_by_shape(NDArray<int32_t> *ndarray)
{
set_strides_by_shape(ndarray);
}
void __nac3_ndarray_set_strides_by_shape64(NDArray<int64_t> *ndarray)
{
set_strides_by_shape(ndarray);
}
void __nac3_ndarray_copy_data(NDArray<int32_t> *src_ndarray, NDArray<int32_t> *dst_ndarray)
{
copy_data(src_ndarray, dst_ndarray);
}
void __nac3_ndarray_copy_data64(NDArray<int64_t> *src_ndarray, NDArray<int64_t> *dst_ndarray)
{
copy_data(src_ndarray, dst_ndarray);
}
}

188
nac3core/irrt/irrt/ndarray/broadcast.hpp
View File

@ -1,188 +0,0 @@
#pragma once
#include <irrt/int_types.hpp>
#include <irrt/ndarray/def.hpp>
#include <irrt/slice.hpp>
namespace
{
template <typename SizeT> struct ShapeEntry
{
SizeT ndims;
SizeT *shape;
};
} // namespace
namespace
{
namespace ndarray
{
namespace broadcast
{
/**
* @brief Return true if `src_shape` can broadcast to `dst_shape`.
*
* See https://numpy.org/doc/stable/user/basics.broadcasting.html
*/
template <typename SizeT>
bool can_broadcast_shape_to(SizeT target_ndims, const SizeT *target_shape, SizeT src_ndims, const SizeT *src_shape)
{
if (src_ndims > target_ndims)
{
return false;
}
for (SizeT i = 0; i < src_ndims; i++)
{
SizeT target_dim = target_shape[target_ndims - i - 1];
SizeT src_dim = src_shape[src_ndims - i - 1];
if (!(src_dim == 1 || target_dim == src_dim))
{
return false;
}
}
return true;
}
/**
* @brief Performs `np.broadcast_shapes(<shapes>)`
*
* @param num_shapes Number of entries in `shapes`
* @param shapes The list of shape to do `np.broadcast_shapes` on.
* @param dst_ndims The length of `dst_shape`.
* `dst_ndims` must be `max([shape.ndims for shape in shapes])`, but the caller has to calculate it/provide it.
* for this function since they should already know in order to allocate `dst_shape` in the first place.
* @param dst_shape The resulting shape. Must be pre-allocated by the caller. This function calculate the result
* of `np.broadcast_shapes` and write it here.
*/
template <typename SizeT>
void broadcast_shapes(SizeT num_shapes, const ShapeEntry<SizeT> *shapes, SizeT dst_ndims, SizeT *dst_shape)
{
for (SizeT dst_axis = 0; dst_axis < dst_ndims; dst_axis++)
{
dst_shape[dst_axis] = 1;
}
#ifdef IRRT_DEBUG_ASSERT
SizeT max_ndims_found = 0;
#endif
for (SizeT i = 0; i < num_shapes; i++)
{
ShapeEntry<SizeT> entry = shapes[i];
// Check pre-condition: `dst_ndims` must be `max([shape.ndims for shape in shapes])`
debug_assert(SizeT, entry.ndims <= dst_ndims);
#ifdef IRRT_DEBUG_ASSERT
max_ndims_found = max(max_ndims_found, entry.ndims);
#endif
for (SizeT j = 0; j < entry.ndims; j++)
{
SizeT entry_axis = entry.ndims - j - 1;
SizeT dst_axis = dst_ndims - j - 1;
SizeT entry_dim = entry.shape[entry_axis];
SizeT dst_dim = dst_shape[dst_axis];
if (dst_dim == 1)
{
dst_shape[dst_axis] = entry_dim;
}
else if (entry_dim == 1 || entry_dim == dst_dim)
{
// Do nothing
}
else
{
raise_exception(SizeT, EXN_VALUE_ERROR,
"shape mismatch: objects cannot be broadcast "
"to a single shape.",
NO_PARAM, NO_PARAM, NO_PARAM);
}
}
}
// Check pre-condition: `dst_ndims` must be `max([shape.ndims for shape in shapes])`
debug_assert_eq(SizeT, max_ndims_found, dst_ndims);
}
/**
* @brief Perform `np.broadcast_to(<ndarray>, <target_shape>)` and appropriate assertions.
*
* This function attempts to broadcast `src_ndarray` to a new shape defined by `dst_ndarray.shape`,
* and return the result by modifying `dst_ndarray`.
*
* # Notes on `dst_ndarray`
* The caller is responsible for allocating space for the resulting ndarray.
* Here is what this function expects from `dst_ndarray` when called:
* - `dst_ndarray->data` does not have to be initialized.
* - `dst_ndarray->itemsize` does not have to be initialized.
* - `dst_ndarray->ndims` must be initialized, determining the length of `dst_ndarray->shape`
* - `dst_ndarray->shape` must be allocated, and must contain the desired target broadcast shape.
* - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
* When this function call ends:
* - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
* - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
* - `dst_ndarray->ndims` is unchanged.
* - `dst_ndarray->shape` is unchanged.
* - `dst_ndarray->strides` is updated accordingly by how ndarray broadcast_to works.
*/
template <typename SizeT> void broadcast_to(const NDArray<SizeT> *src_ndarray, NDArray<SizeT> *dst_ndarray)
{
if (!ndarray::broadcast::can_broadcast_shape_to(dst_ndarray->ndims, dst_ndarray->shape, src_ndarray->ndims,
src_ndarray->shape))
{
raise_exception(SizeT, EXN_VALUE_ERROR, "operands could not be broadcast together", NO_PARAM, NO_PARAM,
NO_PARAM);
}
dst_ndarray->data = src_ndarray->data;
dst_ndarray->itemsize = src_ndarray->itemsize;
for (SizeT i = 0; i < dst_ndarray->ndims; i++)
{
SizeT src_axis = src_ndarray->ndims - i - 1;
SizeT dst_axis = dst_ndarray->ndims - i - 1;
if (src_axis < 0 || (src_ndarray->shape[src_axis] == 1 && dst_ndarray->shape[dst_axis] != 1))
{
// Freeze the steps in-place
dst_ndarray->strides[dst_axis] = 0;
}
else
{
dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
}
}
}
} // namespace broadcast
} // namespace ndarray
} // namespace
extern "C"
{
using namespace ndarray::broadcast;
void __nac3_ndarray_broadcast_to(NDArray<int32_t> *src_ndarray, NDArray<int32_t> *dst_ndarray)
{
broadcast_to(src_ndarray, dst_ndarray);
}
void __nac3_ndarray_broadcast_to64(NDArray<int64_t> *src_ndarray, NDArray<int64_t> *dst_ndarray)
{
broadcast_to(src_ndarray, dst_ndarray);
}
void __nac3_ndarray_broadcast_shapes(int32_t num_shapes, const ShapeEntry<int32_t> *shapes, int32_t dst_ndims,
int32_t *dst_shape)
{
broadcast_shapes(num_shapes, shapes, dst_ndims, dst_shape);
}
void __nac3_ndarray_broadcast_shapes64(int64_t num_shapes, const ShapeEntry<int64_t> *shapes, int64_t dst_ndims,
int64_t *dst_shape)
{
broadcast_shapes(num_shapes, shapes, dst_ndims, dst_shape);
}
}

47
nac3core/irrt/irrt/ndarray/def.hpp
View File

@ -1,47 +0,0 @@
#pragma once
#include <irrt/int_types.hpp>
namespace
{
/**
* @brief The NDArray object
*
* The official numpy implementations: https://github.com/numpy/numpy/blob/735a477f0bc2b5b84d0e72d92f224bde78d4e069/doc/source/reference/c-api/types-and-structures.rst
*/
template <typename SizeT> struct NDArray
{
/**
* @brief The underlying data this `ndarray` is pointing to.
*
* Must be set to `nullptr` to indicate that this NDArray's `data` is uninitialized.
*/
uint8_t *data;
/**
* @brief The number of bytes of a single element in `data`.
*/
SizeT itemsize;
/**
* @brief The number of dimensions of this shape.
*/
SizeT ndims;
/**
* @brief The NDArray shape, with length equal to `ndims`.
*
* Note that it may contain 0.
*/
SizeT *shape;
/**
* @brief Array strides, with length equal to `ndims`
*
* The stride values are in units of bytes, not number of elements.
*
* Note that `strides` can have negative values.
*/
SizeT *strides;
};
} // namespace

243
nac3core/irrt/irrt/ndarray/indexing.hpp
View File

@ -1,243 +0,0 @@
#pragma once
#include <irrt/exception.hpp>
#include <irrt/int_types.hpp>
#include <irrt/ndarray/basic.hpp>
#include <irrt/ndarray/def.hpp>
#include <irrt/range.hpp>
#include <irrt/slice.hpp>
namespace
{
typedef uint8_t NDIndexType;
/**
* @brief A single element index
*
* `data` points to a `int32_t`.
*/
const NDIndexType ND_INDEX_TYPE_SINGLE_ELEMENT = 0;
/**
* @brief A slice index
*
* `data` points to a `Slice<int32_t>`.
*/
const NDIndexType ND_INDEX_TYPE_SLICE = 1;
/**
* @brief `np.newaxis` / `None`
*
* `data` is unused.
*/
const NDIndexType ND_INDEX_TYPE_NEWAXIS = 2;
/**
* @brief `Ellipsis` / `...`
*
* `data` is unused.
*/
const NDIndexType ND_INDEX_TYPE_ELLIPSIS = 3;
/**
* @brief An index used in ndarray indexing
*/
struct NDIndex
{
/**
* @brief Enum tag to specify the type of index.
*
* Please see comments of each enum constant.
*/
NDIndexType type;
/**
* @brief The accompanying data associated with `type`.
*
* Please see comments of each enum constant.
*/
uint8_t *data;
};
} // namespace
namespace
{
namespace ndarray
{
namespace indexing
{
/**
* @brief Perform ndarray "basic indexing" (https://numpy.org/doc/stable/user/basics.indexing.html#basic-indexing)
*
* This function is very similar to performing `dst_ndarray = src_ndarray[indices]` in Python.
*
* This function also does proper assertions on `indices` to check for out of bounds access.
*
* # Notes on `dst_ndarray`
* The caller is responsible for allocating space for the resulting ndarray.
* Here is what this function expects from `dst_ndarray` when called:
* - `dst_ndarray->data` does not have to be initialized.
* - `dst_ndarray->itemsize` does not have to be initialized.
* - `dst_ndarray->ndims` must be initialized, and it must be equal to the expected `ndims` of the `dst_ndarray` after
* indexing `src_ndarray` with `indices`.
* - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
* - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
* When this function call ends:
* - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
* - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
* - `dst_ndarray->ndims` is unchanged.
* - `dst_ndarray->shape` is updated according to how `src_ndarray` is indexed.
* - `dst_ndarray->strides` is updated accordingly by how ndarray indexing works.
*
* @param indices indices to index `src_ndarray`, ordered in the same way you would write them in Python.
* @param src_ndarray The NDArray to be indexed.
* @param dst_ndarray The resulting NDArray after indexing. Further details in the comments above,
*/
template <typename SizeT>
void index(SizeT num_indices, const NDIndex *indices, const NDArray<SizeT> *src_ndarray, NDArray<SizeT> *dst_ndarray)
{
// Validate `indices`.
// Expected value of `dst_ndarray->ndims`.
SizeT expected_dst_ndims = src_ndarray->ndims;
// To check for "too many indices for array: array is ?-dimensional, but ? were indexed"
SizeT num_indexed = 0;
// There may be ellipsis `...` in `indices`. There can only be 0 or 1 ellipsis.
SizeT num_ellipsis = 0;
for (SizeT i = 0; i < num_indices; i++)
{
if (indices[i].type == ND_INDEX_TYPE_SINGLE_ELEMENT)
{
expected_dst_ndims--;
num_indexed++;
}
else if (indices[i].type == ND_INDEX_TYPE_SLICE)
{
num_indexed++;
}
else if (indices[i].type == ND_INDEX_TYPE_NEWAXIS)
{
expected_dst_ndims++;
}
else if (indices[i].type == ND_INDEX_TYPE_ELLIPSIS)
{
num_ellipsis++;
if (num_ellipsis > 1)
{
raise_exception(SizeT, EXN_INDEX_ERROR, "an index can only have a single ellipsis ('...')", NO_PARAM,
NO_PARAM, NO_PARAM);
}
}
else
{
__builtin_unreachable();
}
}
debug_assert_eq(SizeT, expected_dst_ndims, dst_ndarray->ndims);
if (src_ndarray->ndims - num_indexed < 0)
{
raise_exception(SizeT, EXN_INDEX_ERROR,
"too many indices for array: array is {0}-dimensional, "
"but {1} were indexed",
src_ndarray->ndims, num_indices, NO_PARAM);
}
dst_ndarray->data = src_ndarray->data;
dst_ndarray->itemsize = src_ndarray->itemsize;
// Reference code: https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L652
SizeT src_axis = 0;
SizeT dst_axis = 0;
for (int32_t i = 0; i < num_indices; i++)
{
const NDIndex *index = &indices[i];
if (index->type == ND_INDEX_TYPE_SINGLE_ELEMENT)
{
SizeT input = (SizeT) * ((int32_t *)index->data);
SizeT k = slice::resolve_index_in_length(src_ndarray->shape[src_axis], input);
if (k == -1)
{
raise_exception(SizeT, EXN_INDEX_ERROR,
"index {0} is out of bounds for axis {1} "
"with size {2}",
input, src_axis, src_ndarray->shape[src_axis]);
}
dst_ndarray->data += k * src_ndarray->strides[src_axis];
src_axis++;
}
else if (index->type == ND_INDEX_TYPE_SLICE)
{
Slice<int32_t> *slice = (Slice<int32_t> *)index->data;
Range<int32_t> range = slice->indices_checked<SizeT>(src_ndarray->shape[src_axis]);
dst_ndarray->data += (SizeT)range.start * src_ndarray->strides[src_axis];
dst_ndarray->strides[dst_axis] = ((SizeT)range.step) * src_ndarray->strides[src_axis];
dst_ndarray->shape[dst_axis] = (SizeT)range.len<SizeT>();
dst_axis++;
src_axis++;
}
else if (index->type == ND_INDEX_TYPE_NEWAXIS)
{
dst_ndarray->strides[dst_axis] = 0;
dst_ndarray->shape[dst_axis] = 1;
dst_axis++;
}
else if (index->type == ND_INDEX_TYPE_ELLIPSIS)
{
// The number of ':' entries this '...' implies.
SizeT ellipsis_size = src_ndarray->ndims - num_indexed;
for (SizeT j = 0; j < ellipsis_size; j++)
{
dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
dst_axis++;
src_axis++;
}
}
else
{
__builtin_unreachable();
}
}
for (; dst_axis < dst_ndarray->ndims; dst_axis++, src_axis++)
{
dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
}
debug_assert_eq(SizeT, src_ndarray->ndims, src_axis);
debug_assert_eq(SizeT, dst_ndarray->ndims, dst_axis);
}
} // namespace indexing
} // namespace ndarray
} // namespace
extern "C"
{
using namespace ndarray::indexing;
void __nac3_ndarray_index(int32_t num_indices, NDIndex *indices, NDArray<int32_t> *src_ndarray,
NDArray<int32_t> *dst_ndarray)
{
index(num_indices, indices, src_ndarray, dst_ndarray);
}
void __nac3_ndarray_index64(int64_t num_indices, NDIndex *indices, NDArray<int64_t> *src_ndarray,
NDArray<int64_t> *dst_ndarray)
{
index(num_indices, indices, src_ndarray, dst_ndarray);
}
}

142
nac3core/irrt/irrt/ndarray/iter.hpp
View File

@ -1,142 +0,0 @@
#pragma once
#include <irrt/int_types.hpp>
#include <irrt/ndarray/def.hpp>
namespace
{
/**
* @brief Helper struct to enumerate through an ndarray *efficiently*.
*
* i.e., If `shape` is `[3, 2]`, by repeating `next()`, then you get:
* - `[0, 0]`
* - `[0, 1]`
* - `[1, 0]`
* - `[1, 1]`
* - `[2, 0]`
* - `[2, 1]`
* - end.
*
* Interesting cases:
* - If ndims == 0, there is one enumeration.
* - If shape contains zeroes, there are no enumerations.
*/
template <typename SizeT> struct NDIter
{
SizeT ndims;
SizeT *shape;
SizeT *strides;
/**
* @brief The current indices.
*
* Must be allocated by the caller.
*/
SizeT *indices;
/**
* @brief The nth (0-based) index of the current indices.
*/
SizeT nth;
/**
* @brief Pointer to the current element.
*/
uint8_t *element;
/**
* @brief The product of shape.
*/
SizeT size;
// TODO:: There is something called backstrides to speedup iteration.
// See https://ajcr.net/stride-guide-part-1/, and https://docs.scipy.org/doc/numpy-1.13.0/reference/c-api.types-and-structures.html#c.PyArrayIterObject.PyArrayIterObject.backstrides.
// Maybe LLVM is clever and knows how to optimize.
void initialize(SizeT ndims, SizeT *shape, SizeT *strides, uint8_t *element, SizeT *indices)
{
this->ndims = ndims;
this->shape = shape;
this->strides = strides;
this->indices = indices;
this->element = element;
// Compute size and backstrides
this->size = 1;
for (SizeT i = 0; i < ndims; i++)
{
this->size *= shape[i];
}
for (SizeT axis = 0; axis < ndims; axis++)
indices[axis] = 0;
nth = 0;
}
void initialize_by_ndarray(NDArray<SizeT> *ndarray, SizeT *indices)
{
this->initialize(ndarray->ndims, ndarray->shape, ndarray->strides, ndarray->data, indices);
}
bool has_next()
{
return nth < size;
}
void next()
{
for (SizeT i = 0; i < ndims; i++)
{
SizeT axis = ndims - i - 1;
indices[axis]++;
if (indices[axis] >= shape[axis])
{
indices[axis] = 0;
// TODO: Can be optimized with backstrides.
element -= strides[axis] * (shape[axis] - 1);
}
else
{
element += strides[axis];
break;
}
}
nth++;
}
};
} // namespace
extern "C"
{
void __nac3_nditer_initialize(NDIter<int32_t> *iter, NDArray<int32_t> *ndarray, int32_t *indices)
{
iter->initialize_by_ndarray(ndarray, indices);
}
void __nac3_nditer_initialize64(NDIter<int64_t> *iter, NDArray<int64_t> *ndarray, int64_t *indices)
{
iter->initialize_by_ndarray(ndarray, indices);
}
bool __nac3_nditer_has_next(NDIter<int32_t> *iter)
{
return iter->has_next();
}
bool __nac3_nditer_has_next64(NDIter<int64_t> *iter)
{
return iter->has_next();
}
void __nac3_nditer_next(NDIter<int32_t> *iter)
{
iter->next();
}
void __nac3_nditer_next64(NDIter<int64_t> *iter)
{
iter->next();
}
}

92
nac3core/irrt/irrt/ndarray/matmul.hpp
View File

@ -1,92 +0,0 @@
#pragma once
#include <irrt/debug.hpp>
#include <irrt/exception.hpp>
#include <irrt/int_types.hpp>
#include <irrt/ndarray/basic.hpp>
#include <irrt/ndarray/broadcast.hpp>
#include <irrt/ndarray/iter.hpp>
// NOTE: Everything would be much easier and elegant if einsum is implemented.
namespace
{
namespace ndarray
{
namespace matmul
{
/**
* @brief Perform the broadcast in `np.einsum("...ij,...jk->...ik", a, b)`.
*
* Example:
* Suppose `a_shape == [1, 97, 4, 2]`
* and `b_shape == [99, 98, 1, 2, 5]`,
*
* ...then `new_a_shape == [99, 98, 97, 4, 2]`,
* `new_b_shape == [99, 98, 97, 2, 5]`,
* and `dst_shape == [99, 98, 97, 4, 5]`.
* ^^^^^^^^^^ ^^^^
* (broadcasted) (4x2 @ 2x5 => 4x5)
*
* @param a_ndims Length of `a_shape`.
* @param a_shape Shape of `a`.
* @param b_ndims Length of `b_shape`.
* @param b_shape Shape of `b`.
* @param final_ndims Should be equal to `max(a_ndims, b_ndims)`. This is the length of `new_a_shape`,
* `new_b_shape`, and `dst_shape` - the number of dimensions after broadcasting.
*/
template <typename SizeT>
void calculate_shapes(SizeT a_ndims, SizeT *a_shape, SizeT b_ndims, SizeT *b_shape, SizeT final_ndims,
SizeT *new_a_shape, SizeT *new_b_shape, SizeT *dst_shape)
{
debug_assert(SizeT, a_ndims >= 2);
debug_assert(SizeT, b_ndims >= 2);
debug_assert_eq(SizeT, max(a_ndims, b_ndims), final_ndims);
// Check that a and b are compatible for matmul
if (a_shape[a_ndims - 1] != b_shape[b_ndims - 2])
{
// This is a custom error message. Different from NumPy.
raise_exception(SizeT, EXN_VALUE_ERROR, "Cannot multiply LHS (shape ?x{0}) with RHS (shape {1}x?})",
a_shape[a_ndims - 1], b_shape[b_ndims - 2], NO_PARAM);
}
const SizeT num_entries = 2;
ShapeEntry<SizeT> entries[num_entries] = {{.ndims = a_ndims - 2, .shape = a_shape},
{.ndims = b_ndims - 2, .shape = b_shape}};
// TODO: Optimize this
ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries, final_ndims - 2, new_a_shape);
ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries, final_ndims - 2, new_b_shape);
ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries, final_ndims - 2, dst_shape);
new_a_shape[final_ndims - 2] = a_shape[a_ndims - 2];
new_a_shape[final_ndims - 1] = a_shape[a_ndims - 1];
new_b_shape[final_ndims - 2] = b_shape[b_ndims - 2];
new_b_shape[final_ndims - 1] = b_shape[b_ndims - 1];
dst_shape[final_ndims - 2] = a_shape[a_ndims - 2];
dst_shape[final_ndims - 1] = b_shape[b_ndims - 1];
}
} // namespace matmul
} // namespace ndarray
} // namespace
extern "C"
{
using namespace ndarray::matmul;
void __nac3_ndarray_matmul_calculate_shapes(int32_t a_ndims, int32_t *a_shape, int32_t b_ndims, int32_t *b_shape,
int32_t final_ndims, int32_t *new_a_shape, int32_t *new_b_shape,
int32_t *dst_shape)
{
calculate_shapes(a_ndims, a_shape, b_ndims, b_shape, final_ndims, new_a_shape, new_b_shape, dst_shape);
}
void __nac3_ndarray_matmul_calculate_shapes64(int64_t a_ndims, int64_t *a_shape, int64_t b_ndims, int64_t *b_shape,
int64_t final_ndims, int64_t *new_a_shape, int64_t *new_b_shape,
int64_t *dst_shape)
{
calculate_shapes(a_ndims, a_shape, b_ndims, b_shape, final_ndims, new_a_shape, new_b_shape, dst_shape);
}
}

125
nac3core/irrt/irrt/ndarray/reshape.hpp
View File

@ -1,125 +0,0 @@
#pragma once
#include <irrt/int_types.hpp>
#include <irrt/ndarray/def.hpp>
namespace
{
namespace ndarray
{
namespace reshape
{
/**
* @brief Perform assertions on and resolve unknown dimensions in `new_shape` in `np.reshape(<ndarray>, new_shape)`
*
* If `new_shape` indeed contains unknown dimensions (specified with `-1`, just like numpy), `new_shape` will be
* modified to contain the resolved dimension.
*
* To perform assertions on and resolve unknown dimensions in `new_shape`, we don't need the actual
* `<ndarray>` object itself, but only the `.size` of the `<ndarray>`.
*
* @param size The `.size` of `<ndarray>`
* @param new_ndims Number of elements in `new_shape`
* @param new_shape Target shape to reshape to
*/
template <typename SizeT> void resolve_and_check_new_shape(SizeT size, SizeT new_ndims, SizeT *new_shape)
{
// Is there a -1 in `new_shape`?
bool neg1_exists = false;
// Location of -1, only initialized if `neg1_exists` is true
SizeT neg1_axis_i;
// The computed ndarray size of `new_shape`
SizeT new_size = 1;
for (SizeT axis_i = 0; axis_i < new_ndims; axis_i++)
{
SizeT dim = new_shape[axis_i];
if (dim < 0)
{
if (dim == -1)
{
if (neg1_exists)
{
// Multiple `-1` found. Throw an error.
raise_exception(SizeT, EXN_VALUE_ERROR, "can only specify one unknown dimension", NO_PARAM,
NO_PARAM, NO_PARAM);
}
else
{
neg1_exists = true;
neg1_axis_i = axis_i;
}
}
else
{
// TODO: What? In `np.reshape` any negative dimensions is
// treated like its `-1`.
//
// Try running `np.zeros((3, 4)).reshape((-999, 2))`
//
// It is not documented by numpy.
// Throw an error for now...
raise_exception(SizeT, EXN_VALUE_ERROR, "Found non -1 negative dimension {0} on axis {1}", dim, axis_i,
NO_PARAM);
}
}
else
{
new_size *= dim;
}
}
bool can_reshape;
if (neg1_exists)
{
// Let `x` be the unknown dimension
// Solve `x * <new_size> = <size>`
if (new_size == 0 && size == 0)
{
// `x` has infinitely many solutions
can_reshape = false;
}
else if (new_size == 0 && size != 0)
{
// `x` has no solutions
can_reshape = false;
}
else if (size % new_size != 0)
{
// `x` has no integer solutions
can_reshape = false;
}
else
{
can_reshape = true;
new_shape[neg1_axis_i] = size / new_size; // Resolve dimension
}
}
else
{
can_reshape = (new_size == size);
}
if (!can_reshape)
{
raise_exception(SizeT, EXN_VALUE_ERROR, "cannot reshape array of size {0} into given shape", size, NO_PARAM,
NO_PARAM);
}
}
} // namespace reshape
} // namespace ndarray
} // namespace
extern "C"
{
void __nac3_ndarray_reshape_resolve_and_check_new_shape(int32_t size, int32_t new_ndims, int32_t *new_shape)
{
ndarray::reshape::resolve_and_check_new_shape(size, new_ndims, new_shape);
}
void __nac3_ndarray_reshape_resolve_and_check_new_shape64(int64_t size, int64_t new_ndims, int64_t *new_shape)
{
ndarray::reshape::resolve_and_check_new_shape(size, new_ndims, new_shape);
}
}

155
nac3core/irrt/irrt/ndarray/transpose.hpp
View File

@ -1,155 +0,0 @@
#pragma once
#include <irrt/int_types.hpp>
#include <irrt/ndarray/def.hpp>
#include <irrt/slice.hpp>
/*
* Notes on `np.transpose(<array>, <axes>)`
*
* TODO: `axes`, if specified, can actually contain negative indices,
* but it is not documented in numpy.
*
* Supporting it for now.
*/
namespace
{
namespace ndarray
{
namespace transpose
{
/**
* @brief Do assertions on `<axes>` in `np.transpose(<array>, <axes>)`.
*
* Note that `np.transpose`'s `<axe>` argument is optional. If the argument
* is specified but the user, use this function to do assertions on it.
*
* @param ndims The number of dimensions of `<array>`
* @param num_axes Number of elements in `<axes>` as specified by the user.
* This should be equal to `ndims`. If not, a "ValueError: axes don't match array" is thrown.
* @param axes The user specified `<axes>`.
*/
template <typename SizeT> void assert_transpose_axes(SizeT ndims, SizeT num_axes, const SizeT *axes)
{
if (ndims != num_axes)
{
raise_exception(SizeT, EXN_VALUE_ERROR, "axes don't match array", NO_PARAM, NO_PARAM, NO_PARAM);
}
// TODO: Optimize this
bool *axe_specified = (bool *)__builtin_alloca(sizeof(bool) * ndims);
for (SizeT i = 0; i < ndims; i++)
axe_specified[i] = false;
for (SizeT i = 0; i < ndims; i++)
{
SizeT axis = slice::resolve_index_in_length(ndims, axes[i]);
if (axis == -1)
{
// TODO: numpy actually throws a `numpy.exceptions.AxisError`
raise_exception(SizeT, EXN_VALUE_ERROR, "axis {0} is out of bounds for array of dimension {1}", axis, ndims,
NO_PARAM);
}
if (axe_specified[axis])
{
raise_exception(SizeT, EXN_VALUE_ERROR, "repeated axis in transpose", NO_PARAM, NO_PARAM, NO_PARAM);
}
axe_specified[axis] = true;
}
}
/**
* @brief Create a transpose view of `src_ndarray` and perform proper assertions.
*
* This function is very similar to doing `dst_ndarray = np.transpose(src_ndarray, <axes>)`.
* If `<axes>` is supposed to be `None`, caller can pass in a `nullptr` to `<axes>`.
*
* The transpose view created is returned by modifying `dst_ndarray`.
*
* The caller is responsible for setting up `dst_ndarray` before calling this function.
* Here is what this function expects from `dst_ndarray` when called:
* - `dst_ndarray->data` does not have to be initialized.
* - `dst_ndarray->itemsize` does not have to be initialized.
* - `dst_ndarray->ndims` must be initialized, must be equal to `src_ndarray->ndims`.
* - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
* - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
* When this function call ends:
* - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
* - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
* - `dst_ndarray->ndims` is unchanged
* - `dst_ndarray->shape` is updated according to how `np.transpose` works
* - `dst_ndarray->strides` is updated according to how `np.transpose` works
*
* @param src_ndarray The NDArray to build a transpose view on
* @param dst_ndarray The resulting NDArray after transpose. Further details in the comments above,
* @param num_axes Number of elements in axes. Unused if `axes` is nullptr.
* @param axes Axes permutation. Set it to `nullptr` if `<axes>` is `None`.
*/
template <typename SizeT>
void transpose(const NDArray<SizeT> *src_ndarray, NDArray<SizeT> *dst_ndarray, SizeT num_axes, const SizeT *axes)
{
debug_assert_eq(SizeT, src_ndarray->ndims, dst_ndarray->ndims);
const auto ndims = src_ndarray->ndims;
if (axes != nullptr)
assert_transpose_axes(ndims, num_axes, axes);
dst_ndarray->data = src_ndarray->data;
dst_ndarray->itemsize = src_ndarray->itemsize;
// Check out https://ajcr.net/stride-guide-part-2/ to see how `np.transpose` works behind the scenes.
if (axes == nullptr)
{
// `np.transpose(<array>, axes=None)`
/*
* Minor note: `np.transpose(<array>, axes=None)` is equivalent to
* `np.transpose(<array>, axes=[N-1, N-2, ..., 0])` - basically it
* is reversing the order of strides and shape.
*
* This is a fast implementation to handle this special (but very common) case.
*/
for (SizeT axis = 0; axis < ndims; axis++)
{
dst_ndarray->shape[axis] = src_ndarray->shape[ndims - axis - 1];
dst_ndarray->strides[axis] = src_ndarray->strides[ndims - axis - 1];
}
}
else
{
// `np.transpose(<array>, <axes>)`
// Permute strides and shape according to `axes`, while resolving negative indices in `axes`
for (SizeT axis = 0; axis < ndims; axis++)
{
// `i` cannot be OUT_OF_BOUNDS because of assertions
SizeT i = slice::resolve_index_in_length(ndims, axes[axis]);
dst_ndarray->shape[axis] = src_ndarray->shape[i];
dst_ndarray->strides[axis] = src_ndarray->strides[i];
}
}
}
} // namespace transpose
} // namespace ndarray
} // namespace
extern "C"
{
using namespace ndarray::transpose;
void __nac3_ndarray_transpose(const NDArray<int32_t> *src_ndarray, NDArray<int32_t> *dst_ndarray, int32_t num_axes,
const int32_t *axes)
{
transpose(src_ndarray, dst_ndarray, num_axes, axes);
}
void __nac3_ndarray_transpose64(const NDArray<int64_t> *src_ndarray, NDArray<int64_t> *dst_ndarray,
int64_t num_axes, const int64_t *axes)
{
transpose(src_ndarray, dst_ndarray, num_axes, axes);
}
}

215
nac3core/irrt/irrt/original.hpp
View File

@ -1,215 +0,0 @@
#pragma once
#include <irrt/int_types.hpp>
#include <irrt/math_util.hpp>
// The type of an index or a value describing the length of a range/slice is always `int32_t`.
using SliceIndex = int32_t;
namespace
{
// adapted from GNU Scientific Library: https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
// need to make sure `exp >= 0` before calling this function
template <typename T> T __nac3_int_exp_impl(T base, T exp)
{
T res = 1;
/* repeated squaring method */
do
{
if (exp & 1)
{
res *= base; /* for n odd */
}
exp >>= 1;
base *= base;
} while (exp);
return res;
}
} // namespace
extern "C"
{
#define DEF_nac3_int_exp_(T) \
T __nac3_int_exp_##T(T base, T exp) \
{ \
return __nac3_int_exp_impl(base, exp); \
}
DEF_nac3_int_exp_(int32_t) DEF_nac3_int_exp_(int64_t) DEF_nac3_int_exp_(uint32_t) DEF_nac3_int_exp_(uint64_t)
SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len)
{
if (i < 0)
{
i = len + i;
}
if (i < 0)
{
return 0;
}
else if (i > len)
{
return len;
}
return i;
}
SliceIndex __nac3_range_slice_len(const SliceIndex start, const SliceIndex end, const SliceIndex step)
{
SliceIndex diff = end - start;
if (diff > 0 && step > 0)
{
return ((diff - 1) / step) + 1;
}
else if (diff < 0 && step < 0)
{
return ((diff + 1) / step) + 1;
}
else
{
return 0;
}
}
// Handle list assignment and dropping part of the list when
// both dest_step and src_step are +1.
// - All the index must *not* be out-of-bound or negative,
// - The end index is *inclusive*,
// - The length of src and dest slice size should already
// be checked: if dest.step == 1 then len(src) <= len(dest) else len(src) == len(dest)
SliceIndex __nac3_list_slice_assign_var_size(SliceIndex dest_start, SliceIndex dest_end, SliceIndex dest_step,
uint8_t *dest_arr, SliceIndex dest_arr_len, SliceIndex src_start,
SliceIndex src_end, SliceIndex src_step, uint8_t *src_arr,
SliceIndex src_arr_len, const SliceIndex size)
{
/* if dest_arr_len == 0, do nothing since we do not support extending list */
if (dest_arr_len == 0)
return dest_arr_len;
/* if both step is 1, memmove directly, handle the dropping of the list, and shrink size */
if (src_step == dest_step && dest_step == 1)
{
const SliceIndex src_len = (src_end >= src_start) ? (src_end - src_start + 1) : 0;
const SliceIndex dest_len = (dest_end >= dest_start) ? (dest_end - dest_start + 1) : 0;
if (src_len > 0)
{
__builtin_memmove(dest_arr + dest_start * size, src_arr + src_start * size, src_len * size);
}
if (dest_len > 0)
{
/* dropping */
__builtin_memmove(dest_arr + (dest_start + src_len) * size, dest_arr + (dest_end + 1) * size,
(dest_arr_len - dest_end - 1) * size);
}
/* shrink size */
return dest_arr_len - (dest_len - src_len);
}
/* if two range overlaps, need alloca */
uint8_t need_alloca = (dest_arr == src_arr) && !(max(dest_start, dest_end) < min(src_start, src_end) ||
max(src_start, src_end) < min(dest_start, dest_end));
if (need_alloca)
{
uint8_t *tmp = reinterpret_cast<uint8_t *>(__builtin_alloca(src_arr_len * size));
__builtin_memcpy(tmp, src_arr, src_arr_len * size);
src_arr = tmp;
}
SliceIndex src_ind = src_start;
SliceIndex dest_ind = dest_start;
for (; (src_step > 0) ? (src_ind <= src_end) : (src_ind >= src_end); src_ind += src_step, dest_ind += dest_step)
{
/* for constant optimization */
if (size == 1)
{
__builtin_memcpy(dest_arr + dest_ind, src_arr + src_ind, 1);
}
else if (size == 4)
{
__builtin_memcpy(dest_arr + dest_ind * 4, src_arr + src_ind * 4, 4);
}
else if (size == 8)
{
__builtin_memcpy(dest_arr + dest_ind * 8, src_arr + src_ind * 8, 8);
}
else
{
/* memcpy for var size, cannot overlap after previous alloca */
__builtin_memcpy(dest_arr + dest_ind * size, src_arr + src_ind * size, size);
}
}
/* only dest_step == 1 can we shrink the dest list. */
/* size should be ensured prior to calling this function */
if (dest_step == 1 && dest_end >= dest_start)
{
__builtin_memmove(dest_arr + dest_ind * size, dest_arr + (dest_end + 1) * size,
(dest_arr_len - dest_end - 1) * size);
return dest_arr_len - (dest_end - dest_ind) - 1;
}
return dest_arr_len;
}
int32_t __nac3_isinf(double x)
{
return __builtin_isinf(x);
}
int32_t __nac3_isnan(double x)
{
return __builtin_isnan(x);
}
double tgamma(double arg);
double __nac3_gamma(double z)
{
// Handling for denormals
// | x | Python gamma(x) | C tgamma(x) |
// --- | ----------------- | --------------- | ----------- |
// (1) | nan | nan | nan |
// (2) | -inf | -inf | inf |
// (3) | inf | inf | inf |
// (4) | 0.0 | inf | inf |
// (5) | {-1.0, -2.0, ...} | inf | nan |
// (1)-(3)
if (__builtin_isinf(z) || __builtin_isnan(z))
{
return z;
}
double v = tgamma(z);
// (4)-(5)
return __builtin_isinf(v) || __builtin_isnan(v) ? __builtin_inf() : v;
}
double lgamma(double arg);
double __nac3_gammaln(double x)
{
// libm's handling of value overflows differs from scipy:
// - scipy: gammaln(-inf) -> -inf
// - libm : lgamma(-inf) -> inf
if (__builtin_isinf(x))
{
return x;
}
return lgamma(x);
}
double j0(double x);
double __nac3_j0(double x)
{
// libm's handling of value overflows differs from scipy:
// - scipy: j0(inf) -> nan
// - libm : j0(inf) -> 0.0
if (__builtin_isinf(x))
{
return __builtin_nan("");
}
return j0(x);
}
} // extern "C"

54
nac3core/irrt/irrt/range.hpp
View File

@ -1,54 +0,0 @@
#pragma once
#include <irrt/debug.hpp>
#include <irrt/int_types.hpp>
namespace
{
namespace range
{
template <typename T> T len(T start, T stop, T step)
{
// Reference:
// https://github.com/python/cpython/blob/9dbd12375561a393eaec4b21ee4ac568a407cdb0/Objects/rangeobject.c#L933
if (step > 0 && start < stop)
return 1 + (stop - 1 - start) / step;
else if (step < 0 && start > stop)
return 1 + (start - 1 - stop) / (-step);
else
return 0;
}
} // namespace range
/**
* @brief A Python range.
*/
template <typename T> struct Range
{
T start;
T stop;
T step;
/**
* @brief Calculate the `len()` of this range.
*/
template <typename SizeT> T len()
{
debug_assert(SizeT, step != 0);
return range::len(start, stop, step);
}
};
} // namespace
extern "C"
{
int32_t __nac3_range_len_i32(int32_t start, int32_t stop, int32_t step)
{
return range::len(start, stop, step);
}
int64_t __nac3_range_len_i64(int64_t start, int64_t stop, int64_t step)
{
return range::len(start, stop, step);
}
}

210
nac3core/irrt/irrt/slice.hpp
View File

@ -1,202 +1,28 @@
#pragma once
#include <irrt/debug.hpp>
#include <irrt/exception.hpp>
#include <irrt/int_types.hpp>
#include <irrt/math_util.hpp>
#include <irrt/range.hpp>
#include "irrt/int_types.hpp"
namespace
{
namespace slice
{
/**
* @brief Resolve a slice index under a given length like Python indexing.
*
* In Python, if you have a `list` of length 100, `list[-1]` resolves to
* `list[99]`, so `resolve_index_in_length_clamped(100, -1)` returns `99`.
*
* If `length` is 0, 0 is returned for any value of `index`.
*
* If `index` is out of bounds, clamps the returned value between `0` and
* `length - 1` (inclusive).
*
*/
template <typename T> T resolve_index_in_length_clamped(T length, T index)
{
if (index < 0)
{
return max<T>(length + index, 0);
extern "C" {
SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
if (i < 0) {
i = len + i;
}
else
{
return min<T>(length, index);
if (i < 0) {
return 0;
} else if (i > len) {
return len;
}
return i;
}
/**
* @brief Like `resolve_index_in_length_clamped`, but returns `-1` if `index` is
* out of bounds.
*/
template <typename T> T resolve_index_in_length(T length, T index)
{
T resolved = index < 0 ? length + index : index;
if (0 <= resolved && resolved < length)
{
return resolved;
}
else
{
return -1;
SliceIndex __nac3_range_slice_len(const SliceIndex start, const SliceIndex end, const SliceIndex step) {
SliceIndex diff = end - start;
if (diff > 0 && step > 0) {
return ((diff - 1) / step) + 1;
} else if (diff < 0 && step < 0) {
return ((diff + 1) / step) + 1;
} else {
return 0;
}
}
/**
* @brief Resolve a slice as a range.
*
* In Python, this would be `range(*slice(start, stop, step).indices(length))`.
*/
template <typename T>
void indices(bool start_defined, T start, bool stop_defined, T stop, bool step_defined, T step, T length,
T *range_start, T *range_stop, T *range_step)
{
// Reference:
// https://github.com/python/cpython/blob/main/Objects/sliceobject.c#L388
*range_step = step_defined ? step : 1;
bool step_is_negative = *range_step < 0;
T lower, upper;
if (step_is_negative)
{
lower = -1;
upper = length - 1;
}
else
{
lower = 0;
upper = length;
}
if (start_defined)
{
*range_start = start < 0 ? max(lower, start + length) : min(upper, start);
}
else
{
*range_start = step_is_negative ? upper : lower;
}
if (stop_defined)
{
*range_stop = stop < 0 ? max(lower, stop + length) : min(upper, stop);
}
else
{
*range_stop = step_is_negative ? lower : upper;
}
}
} // namespace slice
/**
* @brief A Python-like slice with **unresolved** indices.
*/
template <typename T> struct Slice
{
bool start_defined;
T start;
bool stop_defined;
T stop;
bool step_defined;
T step;
Slice()
{
this->reset();
}
void reset()
{
this->start_defined = false;
this->stop_defined = false;
this->step_defined = false;
}
void set_start(T start)
{
this->start_defined = true;
this->start = start;
}
void set_stop(T stop)
{
this->stop_defined = true;
this->stop = stop;
}
void set_step(T step)
{
this->step_defined = true;
this->step = step;
}
/**
* @brief Resolve this slice as a range.
*
* In Python, this would be `range(*slice(start, stop,
* step).indices(length))`.
*/
template <typename SizeT> Range<T> indices(T length)
{
// Reference:
// https://github.com/python/cpython/blob/main/Objects/sliceobject.c#L388
debug_assert(SizeT, length >= 0);
Range<T> result;
slice::indices(start_defined, start, stop_defined, stop, step_defined, step, length, &result.start,
&result.stop, &result.step);
return result;
}
/**
* @brief Like `.indices()` but with assertions.
*/
template <typename SizeT> Range<T> indices_checked(T length)
{
// TODO: Switch to `SizeT length`
if (length < 0)
{
raise_exception(SizeT, EXN_VALUE_ERROR, "length should not be negative, got {0}", length, NO_PARAM,
NO_PARAM);
}
if (this->step_defined && this->step == 0)
{
raise_exception(SizeT, EXN_VALUE_ERROR, "slice step cannot be zero", NO_PARAM, NO_PARAM, NO_PARAM);
}
return this->indices<SizeT>(length);
}
};
} // namespace
extern "C"
{
void __nac3_slice_indices_i32(bool start_defined, int32_t start, bool stop_defined, int32_t stop, bool step_defined,
int32_t step, int32_t length, int32_t *range_start, int32_t *range_stop,
int32_t *range_step)
{
slice::indices(start_defined, start, stop_defined, stop, step_defined, step, length, range_start, range_stop,
range_step);
}
void __nac3_slice_indices_i64(bool start_defined, int64_t start, bool stop_defined, int64_t stop, bool step_defined,
int64_t step, int64_t length, int64_t *range_start, int64_t *range_stop,
int64_t *range_step)
{
slice::indices(start_defined, start, stop_defined, stop, step_defined, step, length, range_start, range_stop,
range_step);
}
}

1708
nac3core/src/codegen/builtin_fns.rs
View File

File diff suppressed because it is too large Load Diff

897
nac3core/src/codegen/classes.rs
View File

@ -1,7 +1,12 @@
use crate::codegen::{CodeGenContext, CodeGenerator};
use crate::codegen::{
irrt::{call_ndarray_calc_size, call_ndarray_flatten_index},
llvm_intrinsics::call_int_umin,
stmt::gen_for_callback_incrementing,
CodeGenContext, CodeGenerator,
};
use inkwell::context::Context;
use inkwell::types::{BasicType, StructType};
use inkwell::values::{BasicValue, StructValue};
use inkwell::types::{ArrayType, BasicType, StructType};
use inkwell::values::{ArrayValue, BasicValue, StructValue};
use inkwell::{
types::{AnyTypeEnum, BasicTypeEnum, IntType, PointerType},
values::{BasicValueEnum, IntValue, PointerValue},
@ -873,3 +878,889 @@ impl<'ctx> ArrayLikeIndexer<'ctx> for ListDataProxy<'ctx, '_> {
impl<'ctx> UntypedArrayLikeAccessor<'ctx> for ListDataProxy<'ctx, '_> {}
impl<'ctx> UntypedArrayLikeMutator<'ctx> for ListDataProxy<'ctx, '_> {}
/// Proxy type for a `range` type in LLVM.
#[derive(Debug, PartialEq, Eq, Clone, Copy)]
pub struct RangeType<'ctx> {
ty: PointerType<'ctx>,
}
impl<'ctx> RangeType<'ctx> {
/// Checks whether `llvm_ty` represents a `range` type, returning [Err] if it does not.
pub fn is_type(llvm_ty: PointerType<'ctx>) -> Result<(), String> {
let llvm_range_ty = llvm_ty.get_element_type();
let AnyTypeEnum::ArrayType(llvm_range_ty) = llvm_range_ty else {
return Err(format!("Expected array type for `range` type, got {llvm_range_ty}"));
};
if llvm_range_ty.len() != 3 {
return Err(format!(
"Expected 3 elements for `range` type, got {}",
llvm_range_ty.len()
));
}
let llvm_range_elem_ty = llvm_range_ty.get_element_type();
let Ok(llvm_range_elem_ty) = IntType::try_from(llvm_range_elem_ty) else {
return Err(format!(
"Expected int type for `range` element type, got {llvm_range_elem_ty}"
));
};
if llvm_range_elem_ty.get_bit_width() != 32 {
return Err(format!(
"Expected 32-bit int type for `range` element type, got {}",
llvm_range_elem_ty.get_bit_width()
));
}
Ok(())
}
/// Creates an instance of [`RangeType`].
#[must_use]
pub fn new(ctx: &'ctx Context) -> Self {
let llvm_i32 = ctx.i32_type();
let llvm_range = llvm_i32.array_type(3).ptr_type(AddressSpace::default());
RangeType::from_type(llvm_range)
}
/// Creates an [`RangeType`] from a [`PointerType`].
#[must_use]
pub fn from_type(ptr_ty: PointerType<'ctx>) -> Self {
debug_assert!(Self::is_type(ptr_ty).is_ok());
RangeType { ty: ptr_ty }
}
/// Returns the type of all fields of this `range` type.
#[must_use]
pub fn value_type(&self) -> IntType<'ctx> {
self.as_base_type().get_element_type().into_array_type().get_element_type().into_int_type()
}
}
impl<'ctx> ProxyType<'ctx> for RangeType<'ctx> {
type Base = PointerType<'ctx>;
type Underlying = ArrayType<'ctx>;
type Value = RangeValue<'ctx>;
fn new_value<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
name: Option<&'ctx str>,
) -> Self::Value {
self.create_value(
generator.gen_var_alloc(ctx, self.as_underlying_type().into(), name).unwrap(),
name,
)
}
fn create_value(
&self,
value: <Self::Value as ProxyValue<'ctx>>::Base,
name: Option<&'ctx str>,
) -> Self::Value {
debug_assert_eq!(value.get_type(), self.as_base_type());
RangeValue { value, name }
}
fn as_base_type(&self) -> Self::Base {
self.ty
}
fn as_underlying_type(&self) -> Self::Underlying {
self.as_base_type().get_element_type().into_array_type()
}
}
impl<'ctx> From<RangeType<'ctx>> for PointerType<'ctx> {
fn from(value: RangeType<'ctx>) -> Self {
value.as_base_type()
}
}
/// Proxy type for accessing a `range` value in LLVM.
#[derive(Copy, Clone)]
pub struct RangeValue<'ctx> {
value: PointerValue<'ctx>,
name: Option<&'ctx str>,
}
impl<'ctx> RangeValue<'ctx> {
/// Checks whether `value` is an instance of `range`, returning [Err] if `value` is not an instance.
pub fn is_instance(value: PointerValue<'ctx>) -> Result<(), String> {
RangeType::is_type(value.get_type())
}
/// Creates an [`RangeValue`] from a [`PointerValue`].
#[must_use]
pub fn from_ptr_val(ptr: PointerValue<'ctx>, name: Option<&'ctx str>) -> Self {
debug_assert!(Self::is_instance(ptr).is_ok());
<Self as ProxyValue<'ctx>>::Type::from_type(ptr.get_type()).create_value(ptr, name)
}
fn ptr_to_start(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
let llvm_i32 = ctx.ctx.i32_type();
let var_name = self.name.map(|v| format!("{v}.start.addr")).unwrap_or_default();
unsafe {
ctx.builder
.build_in_bounds_gep(
self.as_base_value(),
&[llvm_i32.const_zero(), llvm_i32.const_int(0, false)],
var_name.as_str(),
)
.unwrap()
}
}
fn ptr_to_end(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
let llvm_i32 = ctx.ctx.i32_type();
let var_name = self.name.map(|v| format!("{v}.end.addr")).unwrap_or_default();
unsafe {
ctx.builder
.build_in_bounds_gep(
self.as_base_value(),
&[llvm_i32.const_zero(), llvm_i32.const_int(1, false)],
var_name.as_str(),
)
.unwrap()
}
}
fn ptr_to_step(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
let llvm_i32 = ctx.ctx.i32_type();
let var_name = self.name.map(|v| format!("{v}.step.addr")).unwrap_or_default();
unsafe {
ctx.builder
.build_in_bounds_gep(
self.as_base_value(),
&[llvm_i32.const_zero(), llvm_i32.const_int(2, false)],
var_name.as_str(),
)
.unwrap()
}
}
/// Stores the `start` value into this instance.
pub fn store_start(&self, ctx: &CodeGenContext<'ctx, '_>, start: IntValue<'ctx>) {
debug_assert_eq!(start.get_type().get_bit_width(), 32);
let pstart = self.ptr_to_start(ctx);
ctx.builder.build_store(pstart, start).unwrap();
}
/// Returns the `start` value of this `range`.
pub fn load_start(&self, ctx: &CodeGenContext<'ctx, '_>, name: Option<&str>) -> IntValue<'ctx> {
let pstart = self.ptr_to_start(ctx);
let var_name = name
.map(ToString::to_string)
.or_else(|| self.name.map(|v| format!("{v}.start")))
.unwrap_or_default();
ctx.builder
.build_load(pstart, var_name.as_str())
.map(BasicValueEnum::into_int_value)
.unwrap()
}
/// Stores the `end` value into this instance.
pub fn store_end(&self, ctx: &CodeGenContext<'ctx, '_>, end: IntValue<'ctx>) {
debug_assert_eq!(end.get_type().get_bit_width(), 32);
let pend = self.ptr_to_end(ctx);
ctx.builder.build_store(pend, end).unwrap();
}
/// Returns the `end` value of this `range`.
pub fn load_end(&self, ctx: &CodeGenContext<'ctx, '_>, name: Option<&str>) -> IntValue<'ctx> {
let pend = self.ptr_to_end(ctx);
let var_name = name
.map(ToString::to_string)
.or_else(|| self.name.map(|v| format!("{v}.end")))
.unwrap_or_default();
ctx.builder.build_load(pend, var_name.as_str()).map(BasicValueEnum::into_int_value).unwrap()
}
/// Stores the `step` value into this instance.
pub fn store_step(&self, ctx: &CodeGenContext<'ctx, '_>, step: IntValue<'ctx>) {
debug_assert_eq!(step.get_type().get_bit_width(), 32);
let pstep = self.ptr_to_step(ctx);
ctx.builder.build_store(pstep, step).unwrap();
}
/// Returns the `step` value of this `range`.
pub fn load_step(&self, ctx: &CodeGenContext<'ctx, '_>, name: Option<&str>) -> IntValue<'ctx> {
let pstep = self.ptr_to_step(ctx);
let var_name = name
.map(ToString::to_string)
.or_else(|| self.name.map(|v| format!("{v}.step")))
.unwrap_or_default();
ctx.builder
.build_load(pstep, var_name.as_str())
.map(BasicValueEnum::into_int_value)
.unwrap()
}
}
impl<'ctx> ProxyValue<'ctx> for RangeValue<'ctx> {
type Base = PointerValue<'ctx>;
type Underlying = ArrayValue<'ctx>;
type Type = RangeType<'ctx>;
fn get_type(&self) -> Self::Type {
RangeType::from_type(self.value.get_type())
}
fn as_base_value(&self) -> Self::Base {
self.value
}
fn as_underlying_value(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
name: Option<&'ctx str>,
) -> Self::Underlying {
ctx.builder
.build_load(self.as_base_value(), name.unwrap_or_default())
.map(BasicValueEnum::into_array_value)
.unwrap()
}
}
impl<'ctx> From<RangeValue<'ctx>> for PointerValue<'ctx> {
fn from(value: RangeValue<'ctx>) -> Self {
value.as_base_value()
}
}
/// Proxy type for a `ndarray` type in LLVM.
#[derive(Debug, PartialEq, Eq, Clone, Copy)]
pub struct NDArrayType<'ctx> {
ty: PointerType<'ctx>,
llvm_usize: IntType<'ctx>,
}
impl<'ctx> NDArrayType<'ctx> {
/// Checks whether `llvm_ty` represents a `ndarray` type, returning [Err] if it does not.
pub fn is_type(llvm_ty: PointerType<'ctx>, llvm_usize: IntType<'ctx>) -> Result<(), String> {
let llvm_ndarray_ty = llvm_ty.get_element_type();
let AnyTypeEnum::StructType(llvm_ndarray_ty) = llvm_ndarray_ty else {
return Err(format!("Expected struct type for `NDArray` type, got {llvm_ndarray_ty}"));
};
if llvm_ndarray_ty.count_fields() != 3 {
return Err(format!(
"Expected 3 fields in `NDArray`, got {}",
llvm_ndarray_ty.count_fields()
));
}
let ndarray_ndims_ty = llvm_ndarray_ty.get_field_type_at_index(0).unwrap();
let Ok(ndarray_ndims_ty) = IntType::try_from(ndarray_ndims_ty) else {
return Err(format!("Expected int type for `ndarray.0`, got {ndarray_ndims_ty}"));
};
if ndarray_ndims_ty.get_bit_width() != llvm_usize.get_bit_width() {
return Err(format!(
"Expected {}-bit int type for `ndarray.0`, got {}-bit int",
llvm_usize.get_bit_width(),
ndarray_ndims_ty.get_bit_width()
));
}
let ndarray_dims_ty = llvm_ndarray_ty.get_field_type_at_index(1).unwrap();
let Ok(ndarray_pdims) = PointerType::try_from(ndarray_dims_ty) else {
return Err(format!("Expected pointer type for `ndarray.1`, got {ndarray_dims_ty}"));
};
let ndarray_dims = ndarray_pdims.get_element_type();
let Ok(ndarray_dims) = IntType::try_from(ndarray_dims) else {
return Err(format!(
"Expected pointer-to-int type for `ndarray.1`, got pointer-to-{ndarray_dims}"
));
};
if ndarray_dims.get_bit_width() != llvm_usize.get_bit_width() {
return Err(format!(
"Expected pointer-to-{}-bit int type for `ndarray.1`, got pointer-to-{}-bit int",
llvm_usize.get_bit_width(),
ndarray_dims.get_bit_width()
));
}
let ndarray_data_ty = llvm_ndarray_ty.get_field_type_at_index(2).unwrap();
let Ok(_) = PointerType::try_from(ndarray_data_ty) else {
return Err(format!("Expected pointer type for `ndarray.2`, got {ndarray_data_ty}"));
};
Ok(())
}
/// Creates an instance of [`ListType`].
#[must_use]
pub fn new<G: CodeGenerator + ?Sized>(
generator: &G,
ctx: &'ctx Context,
dtype: BasicTypeEnum<'ctx>,
) -> Self {
let llvm_usize = generator.get_size_type(ctx);
// struct NDArray { num_dims: size_t, dims: size_t*, data: T* }
//
// * num_dims: Number of dimensions in the array
// * dims: Pointer to an array containing the size of each dimension
// * data: Pointer to an array containing the array data
let llvm_ndarray = ctx
.struct_type(
&[
llvm_usize.into(),
llvm_usize.ptr_type(AddressSpace::default()).into(),
dtype.ptr_type(AddressSpace::default()).into(),
],
false,
)
.ptr_type(AddressSpace::default());
NDArrayType::from_type(llvm_ndarray, llvm_usize)
}
/// Creates an [`NDArrayType`] from a [`PointerType`].
#[must_use]
pub fn from_type(ptr_ty: PointerType<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
debug_assert!(Self::is_type(ptr_ty, llvm_usize).is_ok());
NDArrayType { ty: ptr_ty, llvm_usize }
}
/// Returns the type of the `size` field of this `ndarray` type.
#[must_use]
pub fn size_type(&self) -> IntType<'ctx> {
self.as_base_type()
.get_element_type()
.into_struct_type()
.get_field_type_at_index(0)
.map(BasicTypeEnum::into_int_type)
.unwrap()
}
/// Returns the element type of this `ndarray` type.
#[must_use]
pub fn element_type(&self) -> AnyTypeEnum<'ctx> {
self.as_base_type()
.get_element_type()
.into_struct_type()
.get_field_type_at_index(2)
.map(BasicTypeEnum::into_pointer_type)
.map(PointerType::get_element_type)
.unwrap()
}
}
impl<'ctx> ProxyType<'ctx> for NDArrayType<'ctx> {
type Base = PointerType<'ctx>;
type Underlying = StructType<'ctx>;
type Value = NDArrayValue<'ctx>;
fn new_value<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
name: Option<&'ctx str>,
) -> Self::Value {
self.create_value(
generator.gen_var_alloc(ctx, self.as_underlying_type().into(), name).unwrap(),
name,
)
}
fn create_value(
&self,
value: <Self::Value as ProxyValue<'ctx>>::Base,
name: Option<&'ctx str>,
) -> Self::Value {
debug_assert_eq!(value.get_type(), self.as_base_type());
NDArrayValue { value, llvm_usize: self.llvm_usize, name }
}
fn as_base_type(&self) -> Self::Base {
self.ty
}
fn as_underlying_type(&self) -> Self::Underlying {
self.as_base_type().get_element_type().into_struct_type()
}
}
impl<'ctx> From<NDArrayType<'ctx>> for PointerType<'ctx> {
fn from(value: NDArrayType<'ctx>) -> Self {
value.as_base_type()
}
}
/// Proxy type for accessing an `NDArray` value in LLVM.
#[derive(Copy, Clone)]
pub struct NDArrayValue<'ctx> {
value: PointerValue<'ctx>,
llvm_usize: IntType<'ctx>,
name: Option<&'ctx str>,
}
impl<'ctx> NDArrayValue<'ctx> {
/// Checks whether `value` is an instance of `NDArray`, returning [Err] if `value` is not an
/// instance.
pub fn is_instance(value: PointerValue<'ctx>, llvm_usize: IntType<'ctx>) -> Result<(), String> {
NDArrayType::is_type(value.get_type(), llvm_usize)
}
/// Creates an [`NDArrayValue`] from a [`PointerValue`].
#[must_use]
pub fn from_ptr_val(
ptr: PointerValue<'ctx>,
llvm_usize: IntType<'ctx>,
name: Option<&'ctx str>,
) -> Self {
debug_assert!(Self::is_instance(ptr, llvm_usize).is_ok());
<Self as ProxyValue<'ctx>>::Type::from_type(ptr.get_type(), llvm_usize)
.create_value(ptr, name)
}
/// Returns the pointer to the field storing the number of dimensions of this `NDArray`.
fn ptr_to_ndims(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
let llvm_i32 = ctx.ctx.i32_type();
let var_name = self.name.map(|v| format!("{v}.ndims.addr")).unwrap_or_default();
unsafe {
ctx.builder
.build_in_bounds_gep(
self.as_base_value(),
&[llvm_i32.const_zero(), llvm_i32.const_zero()],
var_name.as_str(),
)
.unwrap()
}
}
/// Stores the number of dimensions `ndims` into this instance.
pub fn store_ndims<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
generator: &G,
ndims: IntValue<'ctx>,
) {
debug_assert_eq!(ndims.get_type(), generator.get_size_type(ctx.ctx));
let pndims = self.ptr_to_ndims(ctx);
ctx.builder.build_store(pndims, ndims).unwrap();
}
/// Returns the number of dimensions of this `NDArray` as a value.
pub fn load_ndims(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
let pndims = self.ptr_to_ndims(ctx);
ctx.builder.build_load(pndims, "").map(BasicValueEnum::into_int_value).unwrap()
}
/// Returns the double-indirection pointer to the `dims` array, as if by calling `getelementptr`
/// on the field.
fn ptr_to_dims(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
let llvm_i32 = ctx.ctx.i32_type();
let var_name = self.name.map(|v| format!("{v}.dims.addr")).unwrap_or_default();
unsafe {
ctx.builder
.build_in_bounds_gep(
self.as_base_value(),
&[llvm_i32.const_zero(), llvm_i32.const_int(1, true)],
var_name.as_str(),
)
.unwrap()
}
}
/// Stores the array of dimension sizes `dims` into this instance.
fn store_dim_sizes(&self, ctx: &CodeGenContext<'ctx, '_>, dims: PointerValue<'ctx>) {
ctx.builder.build_store(self.ptr_to_dims(ctx), dims).unwrap();
}
/// Convenience method for creating a new array storing dimension sizes with the given `size`.
pub fn create_dim_sizes(
&self,
ctx: &CodeGenContext<'ctx, '_>,
llvm_usize: IntType<'ctx>,
size: IntValue<'ctx>,
) {
self.store_dim_sizes(ctx, ctx.builder.build_array_alloca(llvm_usize, size, "").unwrap());
}
/// Returns a proxy object to the field storing the size of each dimension of this `NDArray`.
#[must_use]
pub fn dim_sizes(&self) -> NDArrayDimsProxy<'ctx, '_> {
NDArrayDimsProxy(self)
}
/// Returns the double-indirection pointer to the `data` array, as if by calling `getelementptr`
/// on the field.
pub fn ptr_to_data(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
let llvm_i32 = ctx.ctx.i32_type();
let var_name = self.name.map(|v| format!("{v}.data.addr")).unwrap_or_default();
unsafe {
ctx.builder
.build_in_bounds_gep(
self.as_base_value(),
&[llvm_i32.const_zero(), llvm_i32.const_int(2, true)],
var_name.as_str(),
)
.unwrap()
}
}
/// Stores the array of data elements `data` into this instance.
fn store_data(&self, ctx: &CodeGenContext<'ctx, '_>, data: PointerValue<'ctx>) {
ctx.builder.build_store(self.ptr_to_data(ctx), data).unwrap();
}
/// Convenience method for creating a new array storing data elements with the given element
/// type `elem_ty` and `size`.
pub fn create_data(
&self,
ctx: &CodeGenContext<'ctx, '_>,
elem_ty: BasicTypeEnum<'ctx>,
size: IntValue<'ctx>,
) {
self.store_data(ctx, ctx.builder.build_array_alloca(elem_ty, size, "").unwrap());
}
/// Returns a proxy object to the field storing the data of this `NDArray`.
#[must_use]
pub fn data(&self) -> NDArrayDataProxy<'ctx, '_> {
NDArrayDataProxy(self)
}
}
impl<'ctx> ProxyValue<'ctx> for NDArrayValue<'ctx> {
type Base = PointerValue<'ctx>;
type Underlying = StructValue<'ctx>;
type Type = NDArrayType<'ctx>;
fn get_type(&self) -> Self::Type {
NDArrayType::from_type(self.as_base_value().get_type(), self.llvm_usize)
}
fn as_base_value(&self) -> Self::Base {
self.value
}
fn as_underlying_value(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
name: Option<&'ctx str>,
) -> Self::Underlying {
ctx.builder
.build_load(self.as_base_value(), name.unwrap_or_default())
.map(BasicValueEnum::into_struct_value)
.unwrap()
}
}
impl<'ctx> From<NDArrayValue<'ctx>> for PointerValue<'ctx> {
fn from(value: NDArrayValue<'ctx>) -> Self {
value.as_base_value()
}
}
/// Proxy type for accessing the `dims` array of an `NDArray` instance in LLVM.
#[derive(Copy, Clone)]
pub struct NDArrayDimsProxy<'ctx, 'a>(&'a NDArrayValue<'ctx>);
impl<'ctx> ArrayLikeValue<'ctx> for NDArrayDimsProxy<'ctx, '_> {
fn element_type<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
generator: &G,
) -> AnyTypeEnum<'ctx> {
self.0.dim_sizes().base_ptr(ctx, generator).get_type().get_element_type()
}
fn base_ptr<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
_: &G,
) -> PointerValue<'ctx> {
let var_name = self.0.name.map(|v| format!("{v}.data")).unwrap_or_default();
ctx.builder
.build_load(self.0.ptr_to_dims(ctx), var_name.as_str())
.map(BasicValueEnum::into_pointer_value)
.unwrap()
}
fn size<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
_: &G,
) -> IntValue<'ctx> {
self.0.load_ndims(ctx)
}
}
impl<'ctx> ArrayLikeIndexer<'ctx, IntValue<'ctx>> for NDArrayDimsProxy<'ctx, '_> {
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
idx: &IntValue<'ctx>,
name: Option<&str>,
) -> PointerValue<'ctx> {
let var_name = name.map(|v| format!("{v}.addr")).unwrap_or_default();
unsafe {
ctx.builder
.build_in_bounds_gep(self.base_ptr(ctx, generator), &[*idx], var_name.as_str())
.unwrap()
}
}
fn ptr_offset<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
idx: &IntValue<'ctx>,
name: Option<&str>,
) -> PointerValue<'ctx> {
let size = self.size(ctx, generator);
let in_range = ctx.builder.build_int_compare(IntPredicate::ULT, *idx, size, "").unwrap();
ctx.make_assert(
generator,
in_range,
"0:IndexError",
"index {0} is out of bounds for axis 0 with size {1}",
[Some(*idx), Some(self.0.load_ndims(ctx)), None],
ctx.current_loc,
);
unsafe { self.ptr_offset_unchecked(ctx, generator, idx, name) }
}
}
impl<'ctx> UntypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayDimsProxy<'ctx, '_> {}
impl<'ctx> UntypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayDimsProxy<'ctx, '_> {}
impl<'ctx> TypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayDimsProxy<'ctx, '_> {
fn downcast_to_type(
&self,
_: &mut CodeGenContext<'ctx, '_>,
value: BasicValueEnum<'ctx>,
) -> IntValue<'ctx> {
value.into_int_value()
}
}
impl<'ctx> TypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayDimsProxy<'ctx, '_> {
fn upcast_from_type(
&self,
_: &mut CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> BasicValueEnum<'ctx> {
value.into()
}
}
/// Proxy type for accessing the `data` array of an `NDArray` instance in LLVM.
#[derive(Copy, Clone)]
pub struct NDArrayDataProxy<'ctx, 'a>(&'a NDArrayValue<'ctx>);
impl<'ctx> ArrayLikeValue<'ctx> for NDArrayDataProxy<'ctx, '_> {
fn element_type<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
generator: &G,
) -> AnyTypeEnum<'ctx> {
self.0.data().base_ptr(ctx, generator).get_type().get_element_type()
}
fn base_ptr<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
_: &G,
) -> PointerValue<'ctx> {
let var_name = self.0.name.map(|v| format!("{v}.data")).unwrap_or_default();
ctx.builder
.build_load(self.0.ptr_to_data(ctx), var_name.as_str())
.map(BasicValueEnum::into_pointer_value)
.unwrap()
}
fn size<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
generator: &G,
) -> IntValue<'ctx> {
call_ndarray_calc_size(generator, ctx, &self.as_slice_value(ctx, generator), (None, None))
}
}
impl<'ctx> ArrayLikeIndexer<'ctx> for NDArrayDataProxy<'ctx, '_> {
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
idx: &IntValue<'ctx>,
name: Option<&str>,
) -> PointerValue<'ctx> {
unsafe {
ctx.builder
.build_in_bounds_gep(
self.base_ptr(ctx, generator),
&[*idx],
name.unwrap_or_default(),
)
.unwrap()
}
}
fn ptr_offset<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
idx: &IntValue<'ctx>,
name: Option<&str>,
) -> PointerValue<'ctx> {
let data_sz = self.size(ctx, generator);
let in_range = ctx.builder.build_int_compare(IntPredicate::ULT, *idx, data_sz, "").unwrap();
ctx.make_assert(
generator,
in_range,
"0:IndexError",
"index {0} is out of bounds with size {1}",
[Some(*idx), Some(self.0.load_ndims(ctx)), None],
ctx.current_loc,
);
unsafe { self.ptr_offset_unchecked(ctx, generator, idx, name) }
}
}
impl<'ctx> UntypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayDataProxy<'ctx, '_> {}
impl<'ctx> UntypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayDataProxy<'ctx, '_> {}
impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> ArrayLikeIndexer<'ctx, Index>
for NDArrayDataProxy<'ctx, '_>
{
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
indices: &Index,
name: Option<&str>,
) -> PointerValue<'ctx> {
let llvm_usize = generator.get_size_type(ctx.ctx);
let indices_elem_ty = indices
.ptr_offset(ctx, generator, &llvm_usize.const_zero(), None)
.get_type()
.get_element_type();
let Ok(indices_elem_ty) = IntType::try_from(indices_elem_ty) else {
panic!("Expected list[int32] but got {indices_elem_ty}")
};
assert_eq!(
indices_elem_ty.get_bit_width(),
32,
"Expected list[int32] but got list[int{}]",
indices_elem_ty.get_bit_width()
);
let index = call_ndarray_flatten_index(generator, ctx, *self.0, indices);
unsafe {
ctx.builder
.build_in_bounds_gep(
self.base_ptr(ctx, generator),
&[index],
name.unwrap_or_default(),
)
.unwrap()
}
}
fn ptr_offset<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
indices: &Index,
name: Option<&str>,
) -> PointerValue<'ctx> {
let llvm_usize = generator.get_size_type(ctx.ctx);
let indices_size = indices.size(ctx, generator);
let nidx_leq_ndims = ctx
.builder
.build_int_compare(IntPredicate::SLE, indices_size, self.0.load_ndims(ctx), "")
.unwrap();
ctx.make_assert(
generator,
nidx_leq_ndims,
"0:IndexError",
"invalid index to scalar variable",
[None, None, None],
ctx.current_loc,
);
let indices_len = indices.size(ctx, generator);
let ndarray_len = self.0.load_ndims(ctx);
let len = call_int_umin(ctx, indices_len, ndarray_len, None);
gen_for_callback_incrementing(
generator,
ctx,
None,
llvm_usize.const_zero(),
(len, false),
|generator, ctx, _, i| {
let (dim_idx, dim_sz) = unsafe {
(
indices.get_unchecked(ctx, generator, &i, None).into_int_value(),
self.0.dim_sizes().get_typed_unchecked(ctx, generator, &i, None),
)
};
let dim_idx = ctx
.builder
.build_int_z_extend_or_bit_cast(dim_idx, dim_sz.get_type(), "")
.unwrap();
let dim_lt =
ctx.builder.build_int_compare(IntPredicate::SLT, dim_idx, dim_sz, "").unwrap();
ctx.make_assert(
generator,
dim_lt,
"0:IndexError",
"index {0} is out of bounds for axis 0 with size {1}",
[Some(dim_idx), Some(dim_sz), None],
ctx.current_loc,
);
Ok(())
},
llvm_usize.const_int(1, false),
)
.unwrap();
unsafe { self.ptr_offset_unchecked(ctx, generator, indices, name) }
}
}
impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> UntypedArrayLikeAccessor<'ctx, Index>
for NDArrayDataProxy<'ctx, '_>
{
}
impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> UntypedArrayLikeMutator<'ctx, Index>
for NDArrayDataProxy<'ctx, '_>
{
}

893
nac3core/src/codegen/expr.rs
View File

File diff suppressed because it is too large Load Diff

753
nac3core/src/codegen/irrt/mod.rs
View File

@ -1,21 +1,21 @@
use crate::{symbol_resolver::SymbolResolver, typecheck::typedef::Type};
use super::{
classes::{ArrayLikeValue, ListValue},
model::*,
object::{
list::List,
ndarray::{broadcast::ShapeEntry, indexing::NDIndex, nditer::NDIter, NDArray},
classes::{
ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue,
TypedArrayLikeAccessor, TypedArrayLikeAdapter, UntypedArrayLikeAccessor,
},
llvm_intrinsics,
macros::codegen_unreachable,
stmt::gen_for_callback_incrementing,
CodeGenContext, CodeGenerator,
};
use function::CallFunction;
use inkwell::{
attributes::{Attribute, AttributeLoc},
context::Context,
memory_buffer::MemoryBuffer,
module::Module,
types::BasicTypeEnum,
types::{BasicTypeEnum, IntType},
values::{BasicValue, BasicValueEnum, CallSiteValue, FloatValue, IntValue},
AddressSpace, IntPredicate,
};
@ -23,7 +23,7 @@ use itertools::Either;
use nac3parser::ast::Expr;
#[must_use]
pub fn load_irrt(ctx: &Context) -> Module {
pub fn load_irrt<'ctx>(ctx: &'ctx Context, symbol_resolver: &dyn SymbolResolver) -> Module<'ctx> {
let bitcode_buf = MemoryBuffer::create_from_memory_range(
include_bytes!(concat!(env!("OUT_DIR"), "/irrt.bc")),
"irrt_bitcode_buffer",
@ -39,6 +39,25 @@ pub fn load_irrt(ctx: &Context) -> Module {
let function = irrt_mod.get_function(symbol).unwrap();
function.add_attribute(AttributeLoc::Function, ctx.create_enum_attribute(inline_attr, 0));
}
// Initialize all global `EXN_*` exception IDs in IRRT with the [`SymbolResolver`].
let exn_id_type = ctx.i32_type();
let errors = &[
("EXN_INDEX_ERROR", "0:IndexError"),
("EXN_VALUE_ERROR", "0:ValueError"),
("EXN_ASSERTION_ERROR", "0:AssertionError"),
("EXN_TYPE_ERROR", "0:TypeError"),
];
for (irrt_name, symbol_name) in errors {
let exn_id = symbol_resolver.get_string_id(symbol_name);
let exn_id = exn_id_type.const_int(exn_id as u64, false).as_basic_value_enum();
let global = irrt_mod.get_global(irrt_name).unwrap_or_else(|| {
panic!("Exception symbol name '{irrt_name}' should exist in the IRRT LLVM module")
});
global.set_initializer(&exn_id);
}
irrt_mod
}
@ -56,7 +75,7 @@ pub fn integer_power<'ctx, G: CodeGenerator + ?Sized>(
(64, 64, true) => "__nac3_int_exp_int64_t",
(32, 32, false) => "__nac3_int_exp_uint32_t",
(64, 64, false) => "__nac3_int_exp_uint64_t",
_ => unreachable!(),
_ => codegen_unreachable!(ctx),
};
let base_type = base.get_type();
let pow_fun = ctx.module.get_function(symbol).unwrap_or_else(|| {
@ -442,7 +461,7 @@ pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
BasicTypeEnum::IntType(t) => t.size_of(),
BasicTypeEnum::PointerType(t) => t.size_of(),
BasicTypeEnum::StructType(t) => t.size_of().unwrap(),
_ => unreachable!(),
_ => codegen_unreachable!(ctx),
};
ctx.builder.build_int_truncate_or_bit_cast(s, int32, "size").unwrap()
}
@ -564,383 +583,369 @@ pub fn call_j0<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> Flo
.unwrap()
}
// When [`TypeContext::size_type`] is 32-bits, the function name is "{fn_name}".
// When [`TypeContext::size_type`] is 64-bits, the function name is "{fn_name}64".
#[must_use]
pub fn get_sizet_dependent_function_name<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &CodeGenContext<'_, '_>,
name: &str,
) -> String {
let mut name = name.to_owned();
match generator.get_size_type(ctx.ctx).get_bit_width() {
32 => {}
64 => name.push_str("64"),
bit_width => {
panic!("Unsupported int type bit width {bit_width}, must be either 32-bits or 64-bits")
}
}
name
}
/// Generates a call to `__nac3_ndarray_calc_size`. Returns an [`IntValue`] representing the
/// calculated total size.
///
/// * `dims` - An [`ArrayLikeIndexer`] containing the size of each dimension.
/// * `range` - The dimension index to begin and end (exclusively) calculating the dimensions for,
/// or [`None`] if starting from the first dimension and ending at the last dimension
/// respectively.
pub fn call_ndarray_calc_size<'ctx, G, Dims>(
generator: &G,
ctx: &CodeGenContext<'ctx, '_>,
dims: &Dims,
(begin, end): (Option<IntValue<'ctx>>, Option<IntValue<'ctx>>),
) -> IntValue<'ctx>
where
G: CodeGenerator + ?Sized,
Dims: ArrayLikeIndexer<'ctx>,
{
let llvm_usize = generator.get_size_type(ctx.ctx);
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
/// Initialize all global `EXN_*` exception IDs in IRRT with the [`SymbolResolver`].
pub fn setup_irrt_exceptions<'ctx>(
ctx: &'ctx Context,
module: &Module<'ctx>,
symbol_resolver: &dyn SymbolResolver,
) {
let exn_id_type = ctx.i32_type();
let errors = &[
("EXN_INDEX_ERROR", "0:IndexError"),
("EXN_VALUE_ERROR", "0:ValueError"),
("EXN_ASSERTION_ERROR", "0:AssertionError"),
("EXN_TYPE_ERROR", "0:TypeError"),
];
for (irrt_name, symbol_name) in errors {
let exn_id = symbol_resolver.get_string_id(symbol_name);
let exn_id = exn_id_type.const_int(exn_id as u64, false).as_basic_value_enum();
let global = module.get_global(irrt_name).unwrap_or_else(|| {
panic!("Exception symbol name '{irrt_name}' should exist in the IRRT LLVM module")
let ndarray_calc_size_fn_name = match llvm_usize.get_bit_width() {
32 => "__nac3_ndarray_calc_size",
64 => "__nac3_ndarray_calc_size64",
bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
};
let ndarray_calc_size_fn_t = llvm_usize.fn_type(
&[llvm_pusize.into(), llvm_usize.into(), llvm_usize.into(), llvm_usize.into()],
false,
);
let ndarray_calc_size_fn =
ctx.module.get_function(ndarray_calc_size_fn_name).unwrap_or_else(|| {
ctx.module.add_function(ndarray_calc_size_fn_name, ndarray_calc_size_fn_t, None)
});
global.set_initializer(&exn_id);
}
let begin = begin.unwrap_or_else(|| llvm_usize.const_zero());
let end = end.unwrap_or_else(|| dims.size(ctx, generator));
ctx.builder
.build_call(
ndarray_calc_size_fn,
&[
dims.base_ptr(ctx, generator).into(),
dims.size(ctx, generator).into(),
begin.into(),
end.into(),
],
"",
)
.map(CallSiteValue::try_as_basic_value)
.map(|v| v.map_left(BasicValueEnum::into_int_value))
.map(Either::unwrap_left)
.unwrap()
}
pub fn call_nac3_range_len<'ctx, G: CodeGenerator + ?Sized, N: IntKind<'ctx>>(
/// Generates a call to `__nac3_ndarray_calc_nd_indices`. Returns a [`TypeArrayLikeAdpater`]
/// containing `i32` indices of the flattened index.
///
/// * `index` - The index to compute the multidimensional index for.
/// * `ndarray` - LLVM pointer to the `NDArray`. This value must be the LLVM representation of an
/// `NDArray`.
pub fn call_ndarray_calc_nd_indices<'ctx, G: CodeGenerator + ?Sized>(
generator: &G,
ctx: &mut CodeGenContext<'ctx, '_>,
index: IntValue<'ctx>,
ndarray: NDArrayValue<'ctx>,
) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
let llvm_void = ctx.ctx.void_type();
let llvm_i32 = ctx.ctx.i32_type();
let llvm_usize = generator.get_size_type(ctx.ctx);
let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
let ndarray_calc_nd_indices_fn_name = match llvm_usize.get_bit_width() {
32 => "__nac3_ndarray_calc_nd_indices",
64 => "__nac3_ndarray_calc_nd_indices64",
bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
};
let ndarray_calc_nd_indices_fn =
ctx.module.get_function(ndarray_calc_nd_indices_fn_name).unwrap_or_else(|| {
let fn_type = llvm_void.fn_type(
&[llvm_usize.into(), llvm_pusize.into(), llvm_usize.into(), llvm_pi32.into()],
false,
);
ctx.module.add_function(ndarray_calc_nd_indices_fn_name, fn_type, None)
});
let ndarray_num_dims = ndarray.load_ndims(ctx);
let ndarray_dims = ndarray.dim_sizes();
let indices = ctx.builder.build_array_alloca(llvm_i32, ndarray_num_dims, "").unwrap();
ctx.builder
.build_call(
ndarray_calc_nd_indices_fn,
&[
index.into(),
ndarray_dims.base_ptr(ctx, generator).into(),
ndarray_num_dims.into(),
indices.into(),
],
"",
)
.unwrap();
TypedArrayLikeAdapter::from(
ArraySliceValue::from_ptr_val(indices, ndarray_num_dims, None),
Box::new(|_, v| v.into_int_value()),
Box::new(|_, v| v.into()),
)
}
fn call_ndarray_flatten_index_impl<'ctx, G, Indices>(
generator: &G,
ctx: &CodeGenContext<'ctx, '_>,
ndarray: NDArrayValue<'ctx>,
indices: &Indices,
) -> IntValue<'ctx>
where
G: CodeGenerator + ?Sized,
Indices: ArrayLikeIndexer<'ctx>,
{
let llvm_i32 = ctx.ctx.i32_type();
let llvm_usize = generator.get_size_type(ctx.ctx);
let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
debug_assert_eq!(
IntType::try_from(indices.element_type(ctx, generator))
.map(IntType::get_bit_width)
.unwrap_or_default(),
llvm_i32.get_bit_width(),
"Expected i32 value for argument `indices` to `call_ndarray_flatten_index_impl`"
);
debug_assert_eq!(
indices.size(ctx, generator).get_type().get_bit_width(),
llvm_usize.get_bit_width(),
"Expected usize integer value for argument `indices_size` to `call_ndarray_flatten_index_impl`"
);
let ndarray_flatten_index_fn_name = match llvm_usize.get_bit_width() {
32 => "__nac3_ndarray_flatten_index",
64 => "__nac3_ndarray_flatten_index64",
bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
};
let ndarray_flatten_index_fn =
ctx.module.get_function(ndarray_flatten_index_fn_name).unwrap_or_else(|| {
let fn_type = llvm_usize.fn_type(
&[llvm_pusize.into(), llvm_usize.into(), llvm_pi32.into(), llvm_usize.into()],
false,
);
ctx.module.add_function(ndarray_flatten_index_fn_name, fn_type, None)
});
let ndarray_num_dims = ndarray.load_ndims(ctx);
let ndarray_dims = ndarray.dim_sizes();
let index = ctx
.builder
.build_call(
ndarray_flatten_index_fn,
&[
ndarray_dims.base_ptr(ctx, generator).into(),
ndarray_num_dims.into(),
indices.base_ptr(ctx, generator).into(),
indices.size(ctx, generator).into(),
],
"",
)
.map(CallSiteValue::try_as_basic_value)
.map(|v| v.map_left(BasicValueEnum::into_int_value))
.map(Either::unwrap_left)
.unwrap();
index
}
/// Generates a call to `__nac3_ndarray_flatten_index`. Returns the flattened index for the
/// multidimensional index.
///
/// * `ndarray` - LLVM pointer to the `NDArray`. This value must be the LLVM representation of an
/// `NDArray`.
/// * `indices` - The multidimensional index to compute the flattened index for.
pub fn call_ndarray_flatten_index<'ctx, G, Index>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
int_kind: N,
start: Instance<'ctx, Int<N>>,
stop: Instance<'ctx, Int<N>>,
step: Instance<'ctx, Int<N>>,
) -> Instance<'ctx, Int<N>> {
let bit_width = int_kind.get_int_type(generator, ctx.ctx).get_bit_width();
let func_name = match bit_width {
32 => "__nac3_range_len_i32",
64 => "__nac3_range_len_i64",
_ => panic!("{bit_width}-bits ints not supported"), // We could add more variants when necessary.
ndarray: NDArrayValue<'ctx>,
indices: &Index,
) -> IntValue<'ctx>
where
G: CodeGenerator + ?Sized,
Index: ArrayLikeIndexer<'ctx>,
{
call_ndarray_flatten_index_impl(generator, ctx, ndarray, indices)
}
/// Generates a call to `__nac3_ndarray_calc_broadcast`. Returns a tuple containing the number of
/// dimension and size of each dimension of the resultant `ndarray`.
pub fn call_ndarray_calc_broadcast<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
lhs: NDArrayValue<'ctx>,
rhs: NDArrayValue<'ctx>,
) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
let llvm_usize = generator.get_size_type(ctx.ctx);
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
let ndarray_calc_broadcast_fn_name = match llvm_usize.get_bit_width() {
32 => "__nac3_ndarray_calc_broadcast",
64 => "__nac3_ndarray_calc_broadcast64",
bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
};
let ndarray_calc_broadcast_fn =
ctx.module.get_function(ndarray_calc_broadcast_fn_name).unwrap_or_else(|| {
let fn_type = llvm_usize.fn_type(
&[
llvm_pusize.into(),
llvm_usize.into(),
llvm_pusize.into(),
llvm_usize.into(),
llvm_pusize.into(),
],
false,
);
ctx.module.add_function(ndarray_calc_broadcast_fn_name, fn_type, None)
});
let lhs_ndims = lhs.load_ndims(ctx);
let rhs_ndims = rhs.load_ndims(ctx);
let min_ndims = llvm_intrinsics::call_int_umin(ctx, lhs_ndims, rhs_ndims, None);
gen_for_callback_incrementing(
generator,
ctx,
None,
llvm_usize.const_zero(),
(min_ndims, false),
|generator, ctx, _, idx| {
let idx = ctx.builder.build_int_sub(min_ndims, idx, "").unwrap();
let (lhs_dim_sz, rhs_dim_sz) = unsafe {
(
lhs.dim_sizes().get_typed_unchecked(ctx, generator, &idx, None),
rhs.dim_sizes().get_typed_unchecked(ctx, generator, &idx, None),
)
};
CallFunction::begin(generator, ctx, func_name)
.arg(start)
.arg(stop)
.arg(step)
.returning("range_len", Int(int_kind))
let llvm_usize_const_one = llvm_usize.const_int(1, false);
let lhs_eqz = ctx
.builder
.build_int_compare(IntPredicate::EQ, lhs_dim_sz, llvm_usize_const_one, "")
.unwrap();
let rhs_eqz = ctx
.builder
.build_int_compare(IntPredicate::EQ, rhs_dim_sz, llvm_usize_const_one, "")
.unwrap();
let lhs_or_rhs_eqz = ctx.builder.build_or(lhs_eqz, rhs_eqz, "").unwrap();
let lhs_eq_rhs = ctx
.builder
.build_int_compare(IntPredicate::EQ, lhs_dim_sz, rhs_dim_sz, "")
.unwrap();
let is_compatible = ctx.builder.build_or(lhs_or_rhs_eqz, lhs_eq_rhs, "").unwrap();
ctx.make_assert(
generator,
is_compatible,
"0:ValueError",
"operands could not be broadcast together",
[None, None, None],
ctx.current_loc,
);
Ok(())
},
llvm_usize.const_int(1, false),
)
.unwrap();
let max_ndims = llvm_intrinsics::call_int_umax(ctx, lhs_ndims, rhs_ndims, None);
let lhs_dims = lhs.dim_sizes().base_ptr(ctx, generator);
let lhs_ndims = lhs.load_ndims(ctx);
let rhs_dims = rhs.dim_sizes().base_ptr(ctx, generator);
let rhs_ndims = rhs.load_ndims(ctx);
let out_dims = ctx.builder.build_array_alloca(llvm_usize, max_ndims, "").unwrap();
let out_dims = ArraySliceValue::from_ptr_val(out_dims, max_ndims, None);
ctx.builder
.build_call(
ndarray_calc_broadcast_fn,
&[
lhs_dims.into(),
lhs_ndims.into(),
rhs_dims.into(),
rhs_ndims.into(),
out_dims.base_ptr(ctx, generator).into(),
],
"",
)
.unwrap();
TypedArrayLikeAdapter::from(
out_dims,
Box::new(|_, v| v.into_int_value()),
Box::new(|_, v| v.into()),
)
}
#[allow(clippy::too_many_arguments)]
pub fn call_nac3_slice_indices<'ctx, G: CodeGenerator + ?Sized, N: IntKind<'ctx>>(
/// Generates a call to `__nac3_ndarray_calc_broadcast_idx`. Returns an [`ArrayAllocaValue`]
/// containing the indices used for accessing `array` corresponding to the index of the broadcasted
/// array `broadcast_idx`.
pub fn call_ndarray_calc_broadcast_index<
'ctx,
G: CodeGenerator + ?Sized,
BroadcastIdx: UntypedArrayLikeAccessor<'ctx>,
>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
int_kind: N,
start_defined: Instance<'ctx, Int<Bool>>,
start: Instance<'ctx, Int<N>>,
stop_defined: Instance<'ctx, Int<Bool>>,
stop: Instance<'ctx, Int<N>>,
step_defined: Instance<'ctx, Int<Bool>>,
step: Instance<'ctx, Int<N>>,
length: Instance<'ctx, Int<N>>,
range_start: Instance<'ctx, Ptr<Int<N>>>,
range_stop: Instance<'ctx, Ptr<Int<N>>>,
range_step: Instance<'ctx, Ptr<Int<N>>>,
) -> Instance<'ctx, Int<N>> {
let bit_width = int_kind.get_int_type(generator, ctx.ctx).get_bit_width();
let func_name = match bit_width {
32 => "__nac3_slice_indices_i32",
64 => "__nac3_slice_indices_i64",
_ => panic!("{bit_width}-bits ints not supported"), // We could add more variants when necessary.
array: NDArrayValue<'ctx>,
broadcast_idx: &BroadcastIdx,
) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
let llvm_i32 = ctx.ctx.i32_type();
let llvm_usize = generator.get_size_type(ctx.ctx);
let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
let ndarray_calc_broadcast_fn_name = match llvm_usize.get_bit_width() {
32 => "__nac3_ndarray_calc_broadcast_idx",
64 => "__nac3_ndarray_calc_broadcast_idx64",
bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
};
let ndarray_calc_broadcast_fn =
ctx.module.get_function(ndarray_calc_broadcast_fn_name).unwrap_or_else(|| {
let fn_type = llvm_usize.fn_type(
&[llvm_pusize.into(), llvm_usize.into(), llvm_pi32.into(), llvm_pi32.into()],
false,
);
ctx.module.add_function(ndarray_calc_broadcast_fn_name, fn_type, None)
});
let broadcast_size = broadcast_idx.size(ctx, generator);
let out_idx = ctx.builder.build_array_alloca(llvm_i32, broadcast_size, "").unwrap();
let array_dims = array.dim_sizes().base_ptr(ctx, generator);
let array_ndims = array.load_ndims(ctx);
let broadcast_idx_ptr = unsafe {
broadcast_idx.ptr_offset_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
};
CallFunction::begin(generator, ctx, func_name)
.arg(start_defined)
.arg(start)
.arg(stop_defined)
.arg(stop)
.arg(step_defined)
.arg(step)
.arg(length)
.arg(range_start)
.arg(range_stop)
.arg(range_step)
.returning("range_len", Int(int_kind))
}
ctx.builder
.build_call(
ndarray_calc_broadcast_fn,
&[array_dims.into(), array_ndims.into(), broadcast_idx_ptr.into(), out_idx.into()],
"",
)
.unwrap();
pub fn call_nac3_ndarray_util_assert_shape_no_negative<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndims: Instance<'ctx, Int<SizeT>>,
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name = get_sizet_dependent_function_name(
generator,
ctx,
"__nac3_ndarray_util_assert_shape_no_negative",
);
CallFunction::begin(generator, ctx, &name).arg(ndims).arg(shape).returning_void();
}
pub fn call_nac3_ndarray_util_assert_output_shape_same<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray_ndims: Instance<'ctx, Int<SizeT>>,
ndarray_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
output_ndims: Instance<'ctx, Int<SizeT>>,
output_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name = get_sizet_dependent_function_name(
generator,
ctx,
"__nac3_ndarray_util_assert_output_shape_same",
);
CallFunction::begin(generator, ctx, &name)
.arg(ndarray_ndims)
.arg(ndarray_shape)
.arg(output_ndims)
.arg(output_shape)
.returning_void();
}
pub fn call_nac3_ndarray_size<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) -> Instance<'ctx, Int<SizeT>> {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_size");
CallFunction::begin(generator, ctx, &name).arg(ndarray).returning_auto("size")
}
pub fn call_nac3_ndarray_nbytes<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) -> Instance<'ctx, Int<SizeT>> {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_nbytes");
CallFunction::begin(generator, ctx, &name).arg(ndarray).returning_auto("nbytes")
}
pub fn call_nac3_ndarray_len<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) -> Instance<'ctx, Int<SizeT>> {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_len");
CallFunction::begin(generator, ctx, &name).arg(ndarray).returning_auto("len")
}
pub fn call_nac3_ndarray_is_c_contiguous<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) -> Instance<'ctx, Int<Bool>> {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_is_c_contiguous");
CallFunction::begin(generator, ctx, &name).arg(ndarray).returning_auto("is_c_contiguous")
}
pub fn call_nac3_ndarray_get_nth_pelement<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
index: Instance<'ctx, Int<SizeT>>,
) -> Instance<'ctx, Ptr<Int<Byte>>> {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_get_nth_pelement");
CallFunction::begin(generator, ctx, &name).arg(ndarray).arg(index).returning_auto("pelement")
}
pub fn call_nac3_ndarray_get_pelement_by_indices<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
indices: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> Instance<'ctx, Ptr<Int<Byte>>> {
let name =
get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_get_pelement_by_indices");
CallFunction::begin(generator, ctx, &name).arg(ndarray).arg(indices).returning_auto("pelement")
}
pub fn call_nac3_ndarray_set_strides_by_shape<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) {
let name =
get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_set_strides_by_shape");
CallFunction::begin(generator, ctx, &name).arg(ndarray).returning_void();
}
pub fn call_nac3_ndarray_copy_data<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_copy_data");
CallFunction::begin(generator, ctx, &name).arg(src_ndarray).arg(dst_ndarray).returning_void();
}
pub fn call_nac3_nditer_initialize<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
iter: Instance<'ctx, Ptr<Struct<NDIter>>>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
indices: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_nditer_initialize");
CallFunction::begin(generator, ctx, &name).arg(iter).arg(ndarray).arg(indices).returning_void();
}
pub fn call_nac3_nditer_has_next<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
iter: Instance<'ctx, Ptr<Struct<NDIter>>>,
) -> Instance<'ctx, Int<Bool>> {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_nditer_has_next");
CallFunction::begin(generator, ctx, &name).arg(iter).returning_auto("has_next")
}
pub fn call_nac3_nditer_next<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
iter: Instance<'ctx, Ptr<Struct<NDIter>>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_nditer_next");
CallFunction::begin(generator, ctx, &name).arg(iter).returning_void();
}
pub fn call_nac3_ndarray_index<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
num_indices: Instance<'ctx, Int<SizeT>>,
indices: Instance<'ctx, Ptr<Struct<NDIndex>>>,
src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_index");
CallFunction::begin(generator, ctx, &name)
.arg(num_indices)
.arg(indices)
.arg(src_ndarray)
.arg(dst_ndarray)
.returning_void();
}
pub fn call_nac3_ndarray_array_set_and_validate_list_shape<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
list: Instance<'ctx, Ptr<Struct<List<Int<Byte>>>>>,
ndims: Instance<'ctx, Int<SizeT>>,
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name = get_sizet_dependent_function_name(
generator,
ctx,
"__nac3_ndarray_array_set_and_validate_list_shape",
);
CallFunction::begin(generator, ctx, &name).arg(list).arg(ndims).arg(shape).returning_void();
}
pub fn call_nac3_ndarray_array_write_list_to_array<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
list: Instance<'ctx, Ptr<Struct<List<Int<Byte>>>>>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) {
let name = get_sizet_dependent_function_name(
generator,
ctx,
"__nac3_ndarray_array_write_list_to_array",
);
CallFunction::begin(generator, ctx, &name).arg(list).arg(ndarray).returning_void();
}
pub fn call_nac3_ndarray_reshape_resolve_and_check_new_shape<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
size: Instance<'ctx, Int<SizeT>>,
new_ndims: Instance<'ctx, Int<SizeT>>,
new_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name = get_sizet_dependent_function_name(
generator,
ctx,
"__nac3_ndarray_reshape_resolve_and_check_new_shape",
);
CallFunction::begin(generator, ctx, &name)
.arg(size)
.arg(new_ndims)
.arg(new_shape)
.returning_void();
}
pub fn call_nac3_ndarray_broadcast_to<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_to");
CallFunction::begin(generator, ctx, &name).arg(src_ndarray).arg(dst_ndarray).returning_void();
}
pub fn call_nac3_ndarray_broadcast_shapes<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
num_shape_entries: Instance<'ctx, Int<SizeT>>,
shape_entries: Instance<'ctx, Ptr<Struct<ShapeEntry>>>,
dst_ndims: Instance<'ctx, Int<SizeT>>,
dst_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_shapes");
CallFunction::begin(generator, ctx, &name)
.arg(num_shape_entries)
.arg(shape_entries)
.arg(dst_ndims)
.arg(dst_shape)
.returning_void();
}
pub fn call_nac3_ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
num_axes: Instance<'ctx, Int<SizeT>>,
axes: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_transpose");
CallFunction::begin(generator, ctx, &name)
.arg(src_ndarray)
.arg(dst_ndarray)
.arg(num_axes)
.arg(axes)
.returning_void();
}
#[allow(clippy::too_many_arguments)]
pub fn call_nac3_ndarray_matmul_calculate_shapes<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
a_ndims: Instance<'ctx, Int<SizeT>>,
a_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
b_ndims: Instance<'ctx, Int<SizeT>>,
b_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
final_ndims: Instance<'ctx, Int<SizeT>>,
new_a_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
new_b_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
dst_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name =
get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_matmul_calculate_shapes");
CallFunction::begin(generator, ctx, &name)
.arg(a_ndims)
.arg(a_shape)
.arg(b_ndims)
.arg(b_shape)
.arg(final_ndims)
.arg(new_a_shape)
.arg(new_b_shape)
.arg(dst_shape)
.returning_void();
TypedArrayLikeAdapter::from(
ArraySliceValue::from_ptr_val(out_idx, broadcast_size, None),
Box::new(|_, v| v.into_int_value()),
Box::new(|_, v| v.into()),
)
}

4
nac3core/src/codegen/llvm_intrinsics.rs
View File

@ -183,7 +183,7 @@ pub fn call_memcpy_generic<'ctx>(
dest
} else {
ctx.builder
.build_bitcast(dest, llvm_p0i8, "")
.build_bit_cast(dest, llvm_p0i8, "")
.map(BasicValueEnum::into_pointer_value)
.unwrap()
};
@ -191,7 +191,7 @@ pub fn call_memcpy_generic<'ctx>(
src
} else {
ctx.builder
.build_bitcast(src, llvm_p0i8, "")
.build_bit_cast(src, llvm_p0i8, "")
.map(BasicValueEnum::into_pointer_value)
.unwrap()
};

73
nac3core/src/codegen/mod.rs
View File

@ -1,7 +1,7 @@
use crate::{
codegen::classes::{ListType, ProxyType},
codegen::classes::{ListType, NDArrayType, ProxyType, RangeType},
symbol_resolver::{StaticValue, SymbolResolver},
toplevel::{helper::PrimDef, TopLevelContext, TopLevelDef},
toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, TopLevelContext, TopLevelDef},
typecheck::{
type_inferencer::{CodeLocation, PrimitiveStore},
typedef::{CallId, FuncArg, Type, TypeEnum, Unifier},
@ -24,14 +24,7 @@ use inkwell::{
AddressSpace, IntPredicate, OptimizationLevel,
};
use itertools::Itertools;
use model::*;
use nac3parser::ast::{Location, Stmt, StrRef};
use object::{
exception::Exception,
ndarray::NDArray,
range::range_model,
str::{str_model, Str},
};
use parking_lot::{Condvar, Mutex};
use std::collections::{HashMap, HashSet};
use std::sync::{
@ -48,9 +41,7 @@ pub mod extern_fns;
mod generator;
pub mod irrt;
pub mod llvm_intrinsics;
pub mod model;
pub mod numpy;
pub mod object;
pub mod stmt;
#[cfg(test)]
@ -59,6 +50,22 @@ mod test;
use concrete_type::{ConcreteType, ConcreteTypeEnum, ConcreteTypeStore};
pub use generator::{CodeGenerator, DefaultCodeGenerator};
mod macros {
/// Codegen-variant of [`std::unreachable`] which accepts an instance of [`CodeGenContext`] as
/// its first argument to provide Python source information to indicate the codegen location
/// causing the assertion.
macro_rules! codegen_unreachable {
($ctx:expr $(,)?) => {
std::unreachable!("unreachable code while processing {}", &$ctx.current_loc)
};
($ctx:expr, $($arg:tt)*) => {
std::unreachable!("unreachable code while processing {}: {}", &$ctx.current_loc, std::format!("{}", std::format_args!($($arg)+)))
};
}
pub(crate) use codegen_unreachable;
}
#[derive(Default)]
pub struct StaticValueStore {
pub lookup: HashMap<Vec<(usize, u64)>, usize>,
@ -177,11 +184,11 @@ pub struct CodeGenContext<'ctx, 'a> {
pub registry: &'a WorkerRegistry,
/// Cache for constant strings.
pub const_strings: HashMap<String, Instance<'ctx, Str>>,
pub const_strings: HashMap<String, BasicValueEnum<'ctx>>,
/// [`BasicBlock`] containing all `alloca` statements for the current function.
pub init_bb: BasicBlock<'ctx>,
pub exception_val: Option<Instance<'ctx, Ptr<Struct<Exception>>>>,
pub exception_val: Option<PointerValue<'ctx>>,
/// The header and exit basic blocks of a loop in this context. See
/// <https://llvm.org/docs/LoopTerminology.html> for explanation of these terminology.
@ -498,7 +505,12 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
}
TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
Ptr(Struct(NDArray)).get_type(generator, ctx).as_basic_type_enum()
let (dtype, _) = unpack_ndarray_var_tys(unifier, ty);
let element_type = get_llvm_type(
ctx, module, generator, unifier, top_level, type_cache, dtype,
);
NDArrayType::new(generator, ctx, element_type).as_base_type().into()
}
_ => unreachable!(
@ -711,9 +723,36 @@ pub fn gen_func_impl<
(primitives.uint64, context.i64_type().into()),
(primitives.float, context.f64_type().into()),
(primitives.bool, context.i8_type().into()),
(primitives.str, str_model().get_type(generator, context).into()),
(primitives.range, Ptr(range_model()).get_type(generator, context).into()),
(primitives.exception, { Ptr(Struct(Exception)).get_type(generator, context).into() }),
(primitives.str, {
let name = "str";
match module.get_struct_type(name) {
None => {
let str_type = context.opaque_struct_type("str");
let fields = [
context.i8_type().ptr_type(AddressSpace::default()).into(),
generator.get_size_type(context).into(),
];
str_type.set_body(&fields, false);
str_type.into()
}
Some(t) => t.as_basic_type_enum(),
}
}),
(primitives.range, RangeType::new(context).as_base_type().into()),
(primitives.exception, {
let name = "Exception";
if let Some(t) = module.get_struct_type(name) {
t.ptr_type(AddressSpace::default()).as_basic_type_enum()
} else {
let exception = context.opaque_struct_type("Exception");
let int32 = context.i32_type().into();
let int64 = context.i64_type().into();
let str_ty = module.get_struct_type("str").unwrap().as_basic_type_enum();
let fields = [int32, str_ty, int32, int32, str_ty, str_ty, int64, int64, int64];
exception.set_body(&fields, false);
exception.ptr_type(AddressSpace::default()).as_basic_type_enum()
}
}),
]
.iter()
.copied()

42
nac3core/src/codegen/model/any.rs
View File

@ -1,42 +0,0 @@
use inkwell::{
context::Context,
types::{BasicType, BasicTypeEnum},
values::BasicValueEnum,
};
use crate::codegen::CodeGenerator;
use super::*;
/// A [`Model`] of any [`BasicTypeEnum`].
///
/// Use this when you don't need/cannot have any static types to escape from the [`Model`] abstraction.
#[derive(Debug, Clone, Copy)]
pub struct Any<'ctx>(pub BasicTypeEnum<'ctx>);
impl<'ctx> Model<'ctx> for Any<'ctx> {
type Value = BasicValueEnum<'ctx>;
type Type = BasicTypeEnum<'ctx>;
fn get_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
_ctx: &'ctx Context,
) -> Self::Type {
self.0
}
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
&self,
_generator: &mut G,
_ctx: &'ctx Context,
ty: T,
) -> Result<(), ModelError> {
let ty = ty.as_basic_type_enum();
if ty == self.0 {
Ok(())
} else {
Err(ModelError(format!("Expecting {}, but got {}", self.0, ty)))
}
}
}

141
nac3core/src/codegen/model/array.rs
View File

@ -1,141 +0,0 @@
use std::fmt;
use inkwell::{
context::Context,
types::{ArrayType, BasicType, BasicTypeEnum},
values::{ArrayValue, IntValue},
};
use crate::codegen::{CodeGenContext, CodeGenerator};
use super::*;
/// Traits for a Rust struct that describes a length value for [`Array`].
pub trait LenKind: fmt::Debug + Clone + Copy {
fn get_length(&self) -> u32;
}
/// A statically known length.
#[derive(Debug, Clone, Copy, Default)]
pub struct Len<const N: u32>;
/// A dynamically known length.
#[derive(Debug, Clone, Copy)]
pub struct AnyLen(pub u32);
impl<const N: u32> LenKind for Len<N> {
fn get_length(&self) -> u32 {
N
}
}
impl LenKind for AnyLen {
fn get_length(&self) -> u32 {
self.0
}
}
/// A Model for an [`ArrayType`].
#[derive(Debug, Clone, Copy, Default)]
pub struct Array<Len, Item> {
/// Length of this array.
pub len: Len,
/// [`Model`] of an array item.
pub item: Item,
}
impl<'ctx, Len: LenKind, Item: Model<'ctx>> Model<'ctx> for Array<Len, Item> {
type Value = ArrayValue<'ctx>;
type Type = ArrayType<'ctx>;
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
self.item.get_type(generator, ctx).array_type(self.len.get_length())
}
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
ty: T,
) -> Result<(), ModelError> {
let ty = ty.as_basic_type_enum();
let BasicTypeEnum::ArrayType(ty) = ty else {
return Err(ModelError(format!("Expecting ArrayType, but got {ty:?}")));
};
if ty.len() != self.len.get_length() {
return Err(ModelError(format!(
"Expecting ArrayType with size {}, but got an ArrayType with size {}",
ty.len(),
self.len.get_length()
)));
}
self.item
.check_type(generator, ctx, ty.get_element_type())
.map_err(|err| err.under_context("an ArrayType"))?;
Ok(())
}
}
impl<'ctx, Len: LenKind, Item: Model<'ctx>> Instance<'ctx, Ptr<Array<Len, Item>>> {
/// Get the pointer to the `i`-th (0-based) array element.
pub fn gep(
&self,
ctx: &CodeGenContext<'ctx, '_>,
i: IntValue<'ctx>,
) -> Instance<'ctx, Ptr<Item>> {
let zero = ctx.ctx.i32_type().const_zero();
let ptr = unsafe { ctx.builder.build_in_bounds_gep(self.value, &[zero, i], "").unwrap() };
Ptr(self.model.0.item).believe_value(ptr)
}
/// Like `gep` but `i` is a constant.
pub fn gep_const(&self, ctx: &CodeGenContext<'ctx, '_>, i: u64) -> Instance<'ctx, Ptr<Item>> {
assert!(
i < u64::from(self.model.0.len.get_length()),
"Index {i} is out of bounds. Array length = {}",
self.model.0.len.get_length()
);
let i = ctx.ctx.i32_type().const_int(i, false);
self.gep(ctx, i)
}
/// Convenience function equivalent to `.gep(...).load(...)`.
pub fn get<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
i: IntValue<'ctx>,
) -> Instance<'ctx, Item> {
self.gep(ctx, i).load(generator, ctx)
}
/// Like `get` but `i` is a constant.
pub fn get_const<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
i: u64,
) -> Instance<'ctx, Item> {
self.gep_const(ctx, i).load(generator, ctx)
}
/// Convenience function equivalent to `.gep(...).store(...)`.
pub fn set(
&self,
ctx: &CodeGenContext<'ctx, '_>,
i: IntValue<'ctx>,
value: Instance<'ctx, Item>,
) {
self.gep(ctx, i).store(ctx, value);
}
/// Like `set` but `i` is a constant.
pub fn set_const(&self, ctx: &CodeGenContext<'ctx, '_>, i: u64, value: Instance<'ctx, Item>) {
self.gep_const(ctx, i).store(ctx, value);
}
}

212
nac3core/src/codegen/model/core.rs
View File

@ -1,212 +0,0 @@
use std::fmt;
use inkwell::{context::Context, types::*, values::*};
use itertools::Itertools;
use super::*;
use crate::codegen::{CodeGenContext, CodeGenerator};
/// A error type for reporting any [`Model`]-related error (e.g., a [`BasicType`] mismatch).
#[derive(Debug, Clone)]
pub struct ModelError(pub String);
impl ModelError {
// Append a context message to the error.
pub(super) fn under_context(mut self, context: &str) -> Self {
self.0.push_str(" ... in ");
self.0.push_str(context);
self
}
}
/// Trait for Rust structs identifying [`BasicType`]s in the context of a known [`CodeGenerator`] and [`CodeGenContext`].
///
/// For instance,
/// - [`Int<Int32>`] identifies an [`IntType`] with 32-bits.
/// - [`Int<SizeT>`] identifies an [`IntType`] with bit-width [`CodeGenerator::get_size_type`].
/// - [`Ptr<Int<SizeT>>`] identifies a [`PointerType`] that points to an [`IntType`] with bit-width [`CodeGenerator::get_size_type`].
/// - [`Int<AnyInt>`] identifies an [`IntType`] with bit-width of whatever is set in the [`AnyInt`] object.
/// - [`Any`] identifies a [`BasicType`] set in the [`Any`] object itself.
///
/// You can get the [`BasicType`] out of a model with [`Model::get_type`].
///
/// Furthermore, [`Instance<'ctx, M>`] is a simple structure that carries a [`BasicValue`] with a [`BasicType`] identified by model `M`.
///
/// The main purpose of this abstraction is to have a more Rust type-safe way to use Inkwell and give type-hints
/// for programmers.
///
/// ### Notes on `Default` trait
///
/// For some models like [`Int<Int32>`] or [`Int<SizeT>`], they have a [`Default`] trait since just by looking at the type, it is possible
/// to tell which [`BasicType`] they are identifying.
///
/// This can be used to create strongly-typed interfaces accepting only values of a specific [`BasicType`] without having to worry about
/// writing debug assertions to check if the programmer has passed in an [`IntValue`] with the wrong bit-width.
/// ```ignore
/// fn give_me_i32_and_get_a_size_t_back<'ctx>(i32: Instance<'ctx, Int<Int32>>) -> Instance<'ctx, Int<SizeT>> {
/// // code...
/// }
/// ```
///
/// ### Notes on converting between Inkwell and model.
///
/// Suppose you have an [`IntValue`], and you want to pass it into a function that takes a [`Instance<'ctx, Int<Int32>>`]. You can do use
/// [`Model::check_value`] or [`Model::believe_value`].
/// ```ignore
/// let my_value: IntValue<'ctx>;
///
/// let my_value = Int(Int32).check_value(my_value).unwrap(); // Panics if `my_value` is not 32-bit with a descriptive error message.
///
/// // or, if you are absolutely certain that `my_value` is 32-bit and doing extra checks is a waste of time:
/// let my_value = Int(Int32).believe_value(my_value);
/// ```
pub trait Model<'ctx>: fmt::Debug + Clone + Copy {
/// The [`BasicType`] *variant* this model is identifying.
///
/// For [`Int<Int32>`], [`Int<SizeT>`], [`Int<Any>`], etc, this is [`IntValue`];
///
/// For [`Ptr<???>`], etc, this is [`PointerValue`];
///
/// For [`Any`], this is just [`BasicValueEnum`];
///
/// and so on.
type Type: BasicType<'ctx>;
/// The [`BasicValue`] type of the [`BasicType`] of this model.
type Value: BasicValue<'ctx> + TryFrom<BasicValueEnum<'ctx>>;
/// Return the [`BasicType`] of this model.
#[must_use]
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type;
/// Get the number of bytes of the [`BasicType`] of this model.
fn sizeof<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> IntValue<'ctx> {
self.get_type(generator, ctx).size_of().unwrap()
}
/// Check if a [`BasicType`] matches the [`BasicType`] of this model.
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
ty: T,
) -> Result<(), ModelError>;
/// Create an instance from a value with [`Instance::model`] being this model.
///
/// Caller must make sure the type of `value` and the type of this `model` are equivalent.
#[must_use]
fn believe_value(&self, value: Self::Value) -> Instance<'ctx, Self> {
Instance { model: *self, value }
}
/// Check if a [`BasicValue`]'s type is equivalent to the type of this model.
/// Wrap it into an [`Instance`] if it is.
fn check_value<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
value: V,
) -> Result<Instance<'ctx, Self>, ModelError> {
let value = value.as_basic_value_enum();
self.check_type(generator, ctx, value.get_type())
.map_err(|err| err.under_context(format!("the value {value:?}").as_str()))?;
let Ok(value) = Self::Value::try_from(value) else {
unreachable!("check_type() has bad implementation")
};
Ok(self.believe_value(value))
}
// Allocate a value on the stack and return its pointer.
fn alloca<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Ptr<Self>> {
let p = ctx.builder.build_alloca(self.get_type(generator, ctx.ctx), "").unwrap();
Ptr(*self).believe_value(p)
}
// Allocate an array on the stack and return its pointer.
fn array_alloca<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
len: IntValue<'ctx>,
) -> Instance<'ctx, Ptr<Self>> {
let p = ctx.builder.build_array_alloca(self.get_type(generator, ctx.ctx), len, "").unwrap();
Ptr(*self).believe_value(p)
}
fn var_alloca<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
name: Option<&str>,
) -> Result<Instance<'ctx, Ptr<Self>>, String> {
let ty = self.get_type(generator, ctx.ctx).as_basic_type_enum();
let p = generator.gen_var_alloc(ctx, ty, name)?;
Ok(Ptr(*self).believe_value(p))
}
fn array_var_alloca<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
len: IntValue<'ctx>,
name: Option<&'ctx str>,
) -> Result<Instance<'ctx, Ptr<Self>>, String> {
// TODO: Remove ArraySliceValue
let ty = self.get_type(generator, ctx.ctx).as_basic_type_enum();
let p = generator.gen_array_var_alloc(ctx, ty, len, name)?;
Ok(Ptr(*self).believe_value(PointerValue::from(p)))
}
/// Allocate a constant array.
fn const_array<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
values: &[Instance<'ctx, Self>],
) -> Instance<'ctx, Array<AnyLen, Self>> {
macro_rules! make {
($t:expr, $into_value:expr) => {
$t.const_array(
&values
.iter()
.map(|x| $into_value(x.value.as_basic_value_enum()))
.collect_vec(),
)
};
}
let value = match self.get_type(generator, ctx).as_basic_type_enum() {
BasicTypeEnum::ArrayType(t) => make!(t, BasicValueEnum::into_array_value),
BasicTypeEnum::IntType(t) => make!(t, BasicValueEnum::into_int_value),
BasicTypeEnum::FloatType(t) => make!(t, BasicValueEnum::into_float_value),
BasicTypeEnum::PointerType(t) => make!(t, BasicValueEnum::into_pointer_value),
BasicTypeEnum::StructType(t) => make!(t, BasicValueEnum::into_struct_value),
BasicTypeEnum::VectorType(t) => make!(t, BasicValueEnum::into_vector_value),
};
Array { len: AnyLen(values.len() as u32), item: *self }
.check_value(generator, ctx, value)
.unwrap()
}
}
#[derive(Debug, Clone, Copy)]
pub struct Instance<'ctx, M: Model<'ctx>> {
/// The model of this instance.
pub model: M,
/// The value of this instance.
///
/// Caller must make sure the type of `value` and the type of this `model` are equivalent,
/// down to having the same [`IntType::get_bit_width`] in case of [`IntType`] for example.
pub value: M::Value,
}

86
nac3core/src/codegen/model/float.rs
View File

@ -1,86 +0,0 @@
use std::fmt;
use inkwell::{context::Context, types::FloatType, values::FloatValue};
use crate::codegen::CodeGenerator;
use super::*;
pub trait FloatKind<'ctx>: fmt::Debug + Clone + Copy {
fn get_float_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> FloatType<'ctx>;
}
#[derive(Debug, Clone, Copy, Default)]
pub struct Float32;
#[derive(Debug, Clone, Copy, Default)]
pub struct Float64;
impl<'ctx> FloatKind<'ctx> for Float32 {
fn get_float_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
ctx: &'ctx Context,
) -> FloatType<'ctx> {
ctx.f32_type()
}
}
impl<'ctx> FloatKind<'ctx> for Float64 {
fn get_float_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
ctx: &'ctx Context,
) -> FloatType<'ctx> {
ctx.f64_type()
}
}
#[derive(Debug, Clone, Copy)]
pub struct AnyFloat<'ctx>(FloatType<'ctx>);
impl<'ctx> FloatKind<'ctx> for AnyFloat<'ctx> {
fn get_float_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
_ctx: &'ctx Context,
) -> FloatType<'ctx> {
self.0
}
}
#[derive(Debug, Clone, Copy, Default)]
pub struct Float<N>(pub N);
impl<'ctx, N: FloatKind<'ctx>> Model<'ctx> for Float<N> {
type Value = FloatValue<'ctx>;
type Type = FloatType<'ctx>;
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
self.0.get_float_type(generator, ctx)
}
fn check_type<T: inkwell::types::BasicType<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
ty: T,
) -> Result<(), ModelError> {
let ty = ty.as_basic_type_enum();
let Ok(ty) = FloatType::try_from(ty) else {
return Err(ModelError(format!("Expecting FloatType, but got {ty:?}")));
};
let exp_ty = self.0.get_float_type(generator, ctx);
// TODO: Inkwell does not have get_bit_width for FloatType?
if ty != exp_ty {
return Err(ModelError(format!("Expecting {exp_ty:?}, but got {ty:?}")));
}
Ok(())
}
}

103
nac3core/src/codegen/model/function.rs
View File

@ -1,103 +0,0 @@
use inkwell::{
attributes::{Attribute, AttributeLoc},
types::{BasicMetadataTypeEnum, BasicType, FunctionType},
values::{AnyValue, BasicMetadataValueEnum, BasicValue, BasicValueEnum, CallSiteValue},
};
use itertools::Itertools;
use crate::codegen::{CodeGenContext, CodeGenerator};
use super::*;
#[derive(Debug, Clone, Copy)]
struct Arg<'ctx> {
ty: BasicMetadataTypeEnum<'ctx>,
val: BasicMetadataValueEnum<'ctx>,
}
/// A convenience structure to construct & call an LLVM function.
pub struct CallFunction<'ctx, 'a, 'b, 'c, 'd, G: CodeGenerator + ?Sized> {
generator: &'d mut G,
ctx: &'b CodeGenContext<'ctx, 'a>,
/// Function name
name: &'c str,
/// Call arguments
args: Vec<Arg<'ctx>>,
/// LLVM function Attributes
attrs: Vec<&'static str>,
}
impl<'ctx, 'a, 'b, 'c, 'd, G: CodeGenerator + ?Sized> CallFunction<'ctx, 'a, 'b, 'c, 'd, G> {
pub fn begin(generator: &'d mut G, ctx: &'b CodeGenContext<'ctx, 'a>, name: &'c str) -> Self {
CallFunction { generator, ctx, name, args: Vec::new(), attrs: Vec::new() }
}
/// Push a list of LLVM function attributes to the function declaration.
#[must_use]
pub fn attrs(mut self, attrs: Vec<&'static str>) -> Self {
self.attrs = attrs;
self
}
/// Push a call argument to the function call.
#[allow(clippy::needless_pass_by_value)]
#[must_use]
pub fn arg<M: Model<'ctx>>(mut self, arg: Instance<'ctx, M>) -> Self {
let arg = Arg {
ty: arg.model.get_type(self.generator, self.ctx.ctx).as_basic_type_enum().into(),
val: arg.value.as_basic_value_enum().into(),
};
self.args.push(arg);
self
}
/// Call the function and expect the function to return a value of type of `return_model`.
#[must_use]
pub fn returning<M: Model<'ctx>>(self, name: &str, return_model: M) -> Instance<'ctx, M> {
let ret_ty = return_model.get_type(self.generator, self.ctx.ctx);
let ret = self.call(|tys| ret_ty.fn_type(tys, false), name);
let ret = BasicValueEnum::try_from(ret.as_any_value_enum()).unwrap(); // Must work
let ret = return_model.check_value(self.generator, self.ctx.ctx, ret).unwrap(); // Must work
ret
}
/// Like [`CallFunction::returning_`] but `return_model` is automatically inferred.
#[must_use]
pub fn returning_auto<M: Model<'ctx> + Default>(self, name: &str) -> Instance<'ctx, M> {
self.returning(name, M::default())
}
/// Call the function and expect the function to return a void-type.
pub fn returning_void(self) {
let ret_ty = self.ctx.ctx.void_type();
let _ = self.call(|tys| ret_ty.fn_type(tys, false), "");
}
fn call<F>(&self, make_fn_type: F, return_value_name: &str) -> CallSiteValue<'ctx>
where
F: FnOnce(&[BasicMetadataTypeEnum<'ctx>]) -> FunctionType<'ctx>,
{
// Get the LLVM function.
let func = self.ctx.module.get_function(self.name).unwrap_or_else(|| {
// Declare the function if it doesn't exist.
let tys = self.args.iter().map(|arg| arg.ty).collect_vec();
let func_type = make_fn_type(&tys);
let func = self.ctx.module.add_function(self.name, func_type, None);
for attr in &self.attrs {
func.add_attribute(
AttributeLoc::Function,
self.ctx.ctx.create_enum_attribute(Attribute::get_named_enum_kind_id(attr), 0),
);
}
func
});
let vals = self.args.iter().map(|arg| arg.val).collect_vec();
self.ctx.builder.build_call(func, &vals, return_value_name).unwrap()
}
}

417
nac3core/src/codegen/model/int.rs
View File

@ -1,417 +0,0 @@
use std::{cmp::Ordering, fmt};
use inkwell::{
context::Context,
types::{BasicType, IntType},
values::IntValue,
IntPredicate,
};
use crate::codegen::{CodeGenContext, CodeGenerator};
use super::*;
pub trait IntKind<'ctx>: fmt::Debug + Clone + Copy {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx>;
}
#[derive(Debug, Clone, Copy, Default)]
pub struct Bool;
#[derive(Debug, Clone, Copy, Default)]
pub struct Byte;
#[derive(Debug, Clone, Copy, Default)]
pub struct Int32;
#[derive(Debug, Clone, Copy, Default)]
pub struct Int64;
#[derive(Debug, Clone, Copy, Default)]
pub struct SizeT;
impl<'ctx> IntKind<'ctx> for Bool {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx> {
ctx.bool_type()
}
}
impl<'ctx> IntKind<'ctx> for Byte {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx> {
ctx.i8_type()
}
}
impl<'ctx> IntKind<'ctx> for Int32 {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx> {
ctx.i32_type()
}
}
impl<'ctx> IntKind<'ctx> for Int64 {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx> {
ctx.i64_type()
}
}
impl<'ctx> IntKind<'ctx> for SizeT {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx> {
generator.get_size_type(ctx)
}
}
#[derive(Debug, Clone, Copy)]
pub struct AnyInt<'ctx>(pub IntType<'ctx>);
impl<'ctx> IntKind<'ctx> for AnyInt<'ctx> {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
_ctx: &'ctx Context,
) -> IntType<'ctx> {
self.0
}
}
#[derive(Debug, Clone, Copy, Default)]
pub struct Int<N>(pub N);
impl<'ctx, N: IntKind<'ctx>> Model<'ctx> for Int<N> {
type Value = IntValue<'ctx>;
type Type = IntType<'ctx>;
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
self.0.get_int_type(generator, ctx)
}
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
ty: T,
) -> Result<(), ModelError> {
let ty = ty.as_basic_type_enum();
let Ok(ty) = IntType::try_from(ty) else {
return Err(ModelError(format!("Expecting IntType, but got {ty:?}")));
};
let exp_ty = self.0.get_int_type(generator, ctx);
if ty.get_bit_width() != exp_ty.get_bit_width() {
return Err(ModelError(format!(
"Expecting IntType to have {} bit(s), but got {} bit(s)",
exp_ty.get_bit_width(),
ty.get_bit_width()
)));
}
Ok(())
}
}
impl<'ctx, N: IntKind<'ctx>> Int<N> {
pub fn const_int<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
value: u64,
) -> Instance<'ctx, Self> {
let value = self.get_type(generator, ctx).const_int(value, false);
self.believe_value(value)
}
pub fn const_0<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Instance<'ctx, Self> {
let value = self.get_type(generator, ctx).const_zero();
self.believe_value(value)
}
pub fn const_1<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Instance<'ctx, Self> {
self.const_int(generator, ctx, 1)
}
pub fn const_all_ones<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Instance<'ctx, Self> {
let value = self.get_type(generator, ctx).const_all_ones();
self.believe_value(value)
}
pub fn s_extend_or_bit_cast<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
assert!(
value.get_type().get_bit_width()
<= self.0.get_int_type(generator, ctx.ctx).get_bit_width()
);
let value = ctx
.builder
.build_int_s_extend_or_bit_cast(value, self.get_type(generator, ctx.ctx), "")
.unwrap();
self.believe_value(value)
}
pub fn s_extend<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
assert!(
value.get_type().get_bit_width()
< self.0.get_int_type(generator, ctx.ctx).get_bit_width()
);
let value =
ctx.builder.build_int_s_extend(value, self.get_type(generator, ctx.ctx), "").unwrap();
self.believe_value(value)
}
pub fn z_extend_or_bit_cast<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
assert!(
value.get_type().get_bit_width()
<= self.0.get_int_type(generator, ctx.ctx).get_bit_width()
);
let value = ctx
.builder
.build_int_z_extend_or_bit_cast(value, self.get_type(generator, ctx.ctx), "")
.unwrap();
self.believe_value(value)
}
pub fn z_extend<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
assert!(
value.get_type().get_bit_width()
< self.0.get_int_type(generator, ctx.ctx).get_bit_width()
);
let value =
ctx.builder.build_int_z_extend(value, self.get_type(generator, ctx.ctx), "").unwrap();
self.believe_value(value)
}
pub fn truncate_or_bit_cast<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
assert!(
value.get_type().get_bit_width()
>= self.0.get_int_type(generator, ctx.ctx).get_bit_width()
);
let value = ctx
.builder
.build_int_truncate_or_bit_cast(value, self.get_type(generator, ctx.ctx), "")
.unwrap();
self.believe_value(value)
}
pub fn truncate<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
assert!(
value.get_type().get_bit_width()
> self.0.get_int_type(generator, ctx.ctx).get_bit_width()
);
let value =
ctx.builder.build_int_truncate(value, self.get_type(generator, ctx.ctx), "").unwrap();
self.believe_value(value)
}
/// `sext` or `trunc` an int to this model's int type. Does nothing if equal bit-widths.
pub fn s_extend_or_truncate<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
let their_width = value.get_type().get_bit_width();
let our_width = self.0.get_int_type(generator, ctx.ctx).get_bit_width();
match their_width.cmp(&our_width) {
Ordering::Less => self.s_extend(generator, ctx, value),
Ordering::Equal => self.believe_value(value),
Ordering::Greater => self.truncate(generator, ctx, value),
}
}
/// `zext` or `trunc` an int to this model's int type. Does nothing if equal bit-widths.
pub fn z_extend_or_truncate<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
let their_width = value.get_type().get_bit_width();
let our_width = self.0.get_int_type(generator, ctx.ctx).get_bit_width();
match their_width.cmp(&our_width) {
Ordering::Less => self.z_extend(generator, ctx, value),
Ordering::Equal => self.believe_value(value),
Ordering::Greater => self.truncate(generator, ctx, value),
}
}
}
impl Int<Bool> {
#[must_use]
pub fn const_false<'ctx, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Instance<'ctx, Self> {
self.const_int(generator, ctx, 0)
}
#[must_use]
pub fn const_true<'ctx, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Instance<'ctx, Self> {
self.const_int(generator, ctx, 1)
}
}
impl<'ctx, N: IntKind<'ctx>> Instance<'ctx, Int<N>> {
pub fn s_extend_or_bit_cast<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).s_extend_or_bit_cast(generator, ctx, self.value)
}
pub fn s_extend<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).s_extend(generator, ctx, self.value)
}
pub fn z_extend_or_bit_cast<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).z_extend_or_bit_cast(generator, ctx, self.value)
}
pub fn z_extend<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).z_extend(generator, ctx, self.value)
}
pub fn truncate_or_bit_cast<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).truncate_or_bit_cast(generator, ctx, self.value)
}
pub fn truncate<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).truncate(generator, ctx, self.value)
}
pub fn s_extend_or_truncate<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).s_extend_or_truncate(generator, ctx, self.value)
}
pub fn z_extend_or_truncate<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).z_extend_or_truncate(generator, ctx, self.value)
}
#[must_use]
pub fn add(&self, ctx: &CodeGenContext<'ctx, '_>, other: Self) -> Self {
let value = ctx.builder.build_int_add(self.value, other.value, "").unwrap();
self.model.believe_value(value)
}
#[must_use]
pub fn sub(&self, ctx: &CodeGenContext<'ctx, '_>, other: Self) -> Self {
let value = ctx.builder.build_int_sub(self.value, other.value, "").unwrap();
self.model.believe_value(value)
}
#[must_use]
pub fn mul(&self, ctx: &CodeGenContext<'ctx, '_>, other: Self) -> Self {
let value = ctx.builder.build_int_mul(self.value, other.value, "").unwrap();
self.model.believe_value(value)
}
pub fn compare(
&self,
ctx: &CodeGenContext<'ctx, '_>,
op: IntPredicate,
other: Self,
) -> Instance<'ctx, Int<Bool>> {
let value = ctx.builder.build_int_compare(op, self.value, other.value, "").unwrap();
Int(Bool).believe_value(value)
}
}

17
nac3core/src/codegen/model/mod.rs
View File

@ -1,17 +0,0 @@
mod any;
mod array;
mod core;
mod float;
pub mod function;
mod int;
mod ptr;
mod structure;
pub mod util;
pub use any::*;
pub use array::*;
pub use core::*;
pub use float::*;
pub use int::*;
pub use ptr::*;
pub use structure::*;

207
nac3core/src/codegen/model/ptr.rs
View File

@ -1,207 +0,0 @@
use inkwell::{
context::Context,
types::{BasicType, BasicTypeEnum, PointerType},
values::{IntValue, PointerValue},
AddressSpace,
};
use crate::codegen::{llvm_intrinsics::call_memcpy_generic, CodeGenContext, CodeGenerator};
use super::*;
/// A model for [`PointerType`].
// TODO: LLVM 15: `Item` is a Rust type-hint for the LLVM type of value the `.store()/.load()` family
// of functions return. If a truly opaque pointer is needed, tell the programmer to use `OpaquePtr`.
#[derive(Debug, Clone, Copy, Default)]
pub struct Ptr<Item>(pub Item);
/// An opaque pointer. Like [`Ptr`] but without any Rust type-hints about its element type.
///
/// `.load()/.store()` is not available for [`Instance`]s of opaque pointers.
pub type OpaquePtr = Ptr<()>;
// TODO: LLVM 15: `Item: Model<'ctx>` don't even need to be a model anymore. It will only be
// a type-hint for the `.load()/.store()` functions for the `pointee_ty`.
//
// See https://thedan64.github.io/inkwell/inkwell/builder/struct.Builder.html#method.build_load.
impl<'ctx, Item: Model<'ctx>> Model<'ctx> for Ptr<Item> {
type Value = PointerValue<'ctx>;
type Type = PointerType<'ctx>;
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
// TODO: LLVM 15: ctx.ptr_type(AddressSpace::default())
self.0.get_type(generator, ctx).ptr_type(AddressSpace::default())
}
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
ty: T,
) -> Result<(), ModelError> {
let ty = ty.as_basic_type_enum();
let Ok(ty) = PointerType::try_from(ty) else {
return Err(ModelError(format!("Expecting PointerType, but got {ty:?}")));
};
let elem_ty = ty.get_element_type();
let Ok(elem_ty) = BasicTypeEnum::try_from(elem_ty) else {
return Err(ModelError(format!(
"Expecting pointer element type to be a BasicTypeEnum, but got {elem_ty:?}"
)));
};
// TODO: inkwell `get_element_type()` will be deprecated.
// Remove the check for `get_element_type()` when the time comes.
self.0
.check_type(generator, ctx, elem_ty)
.map_err(|err| err.under_context("a PointerType"))?;
Ok(())
}
}
impl<'ctx, Item: Model<'ctx>> Ptr<Item> {
/// Return a ***constant*** nullptr.
pub fn nullptr<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Instance<'ctx, Ptr<Item>> {
let ptr = self.get_type(generator, ctx).const_null();
self.believe_value(ptr)
}
/// Cast a pointer into this model with [`inkwell::builder::Builder::build_pointer_cast`]
pub fn pointer_cast<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
ptr: PointerValue<'ctx>,
) -> Instance<'ctx, Ptr<Item>> {
// TODO: LLVM 15: Write in an impl where `Item` does not have to be `Model<'ctx>`.
// TODO: LLVM 15: This function will only have to be:
// ```
// return self.believe_value(ptr);
// ```
let t = self.get_type(generator, ctx.ctx);
let ptr = ctx.builder.build_pointer_cast(ptr, t, "").unwrap();
self.believe_value(ptr)
}
}
impl<'ctx, Item: Model<'ctx>> Instance<'ctx, Ptr<Item>> {
/// Offset the pointer by [`inkwell::builder::Builder::build_in_bounds_gep`].
#[must_use]
pub fn offset(
&self,
ctx: &CodeGenContext<'ctx, '_>,
offset: IntValue<'ctx>,
) -> Instance<'ctx, Ptr<Item>> {
let p = unsafe { ctx.builder.build_in_bounds_gep(self.value, &[offset], "").unwrap() };
self.model.believe_value(p)
}
/// Offset the pointer by [`inkwell::builder::Builder::build_in_bounds_gep`] by a constant offset.
#[must_use]
pub fn offset_const(
&self,
ctx: &CodeGenContext<'ctx, '_>,
offset: u64,
) -> Instance<'ctx, Ptr<Item>> {
let offset = ctx.ctx.i32_type().const_int(offset, false);
self.offset(ctx, offset)
}
pub fn set_index(
&self,
ctx: &CodeGenContext<'ctx, '_>,
index: IntValue<'ctx>,
value: Instance<'ctx, Item>,
) {
self.offset(ctx, index).store(ctx, value);
}
pub fn set_index_const(
&self,
ctx: &CodeGenContext<'ctx, '_>,
index: u64,
value: Instance<'ctx, Item>,
) {
self.offset_const(ctx, index).store(ctx, value);
}
pub fn get_index<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
index: IntValue<'ctx>,
) -> Instance<'ctx, Item> {
self.offset(ctx, index).load(generator, ctx)
}
pub fn get_index_const<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
index: u64,
) -> Instance<'ctx, Item> {
self.offset_const(ctx, index).load(generator, ctx)
}
/// Load the value with [`inkwell::builder::Builder::build_load`].
pub fn load<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Item> {
let value = ctx.builder.build_load(self.value, "").unwrap();
self.model.0.check_value(generator, ctx.ctx, value).unwrap() // If unwrap() panics, there is a logic error.
}
/// Store a value with [`inkwell::builder::Builder::build_store`].
pub fn store(&self, ctx: &CodeGenContext<'ctx, '_>, value: Instance<'ctx, Item>) {
ctx.builder.build_store(self.value, value.value).unwrap();
}
/// Return a casted pointer of element type `NewElement` with [`inkwell::builder::Builder::build_pointer_cast`].
pub fn pointer_cast<NewItem: Model<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
new_item: NewItem,
) -> Instance<'ctx, Ptr<NewItem>> {
// TODO: LLVM 15: Write in an impl where `Item` does not have to be `Model<'ctx>`.
Ptr(new_item).pointer_cast(generator, ctx, self.value)
}
/// Check if the pointer is null with [`inkwell::builder::Builder::build_is_null`].
pub fn is_null(&self, ctx: &CodeGenContext<'ctx, '_>) -> Instance<'ctx, Int<Bool>> {
let value = ctx.builder.build_is_null(self.value, "").unwrap();
Int(Bool).believe_value(value)
}
/// Check if the pointer is not null with [`inkwell::builder::Builder::build_is_not_null`].
pub fn is_not_null(&self, ctx: &CodeGenContext<'ctx, '_>) -> Instance<'ctx, Int<Bool>> {
let value = ctx.builder.build_is_not_null(self.value, "").unwrap();
Int(Bool).believe_value(value)
}
/// `memcpy` from another pointer.
pub fn copy_from<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
source: Self,
num_items: IntValue<'ctx>,
) {
// Force extend `num_items` and `itemsize` to `i64` so their types would match.
let itemsize = self.model.sizeof(generator, ctx.ctx);
let itemsize = Int(Int64).z_extend_or_truncate(generator, ctx, itemsize);
let num_items = Int(Int64).z_extend_or_truncate(generator, ctx, num_items);
let totalsize = itemsize.mul(ctx, num_items);
let is_volatile = ctx.ctx.bool_type().const_zero(); // is_volatile = false
call_memcpy_generic(ctx, self.value, source.value, totalsize.value, is_volatile);
}
}

345
nac3core/src/codegen/model/structure.rs
View File

@ -1,345 +0,0 @@
use std::fmt;
use inkwell::{
context::Context,
types::{BasicType, BasicTypeEnum, StructType},
values::{BasicValueEnum, StructValue},
};
use crate::codegen::{CodeGenContext, CodeGenerator};
use super::*;
/// A traveral that traverses a Rust `struct` that is used to declare an LLVM's struct's field types.
pub trait FieldTraversal<'ctx> {
/// Output type of [`FieldTraversal::add`].
type Out<M>;
/// Traverse through the type of a declared field and do something with it.
///
/// * `name` - The cosmetic name of the LLVM field. Used for debugging.
/// * `model` - The [`Model`] representing the LLVM type of this field.
fn add<M: Model<'ctx>>(&mut self, name: &'static str, model: M) -> Self::Out<M>;
/// Like [`FieldTraversal::add`] but [`Model`] is automatically inferred from its [`Default`] trait.
fn add_auto<M: Model<'ctx> + Default>(&mut self, name: &'static str) -> Self::Out<M> {
self.add(name, M::default())
}
}
/// Descriptor of an LLVM struct field.
#[derive(Debug, Clone, Copy)]
pub struct GepField<M> {
/// The GEP index of this field. This is the index to use with `build_gep`.
pub gep_index: u64,
/// The cosmetic name of this field.
pub name: &'static str,
/// The [`Model`] of this field's type.
pub model: M,
}
/// A traversal to get the GEP index of fields.
pub struct GepFieldTraversal {
/// The current GEP index.
gep_index_counter: u64,
}
impl<'ctx> FieldTraversal<'ctx> for GepFieldTraversal {
type Out<M> = GepField<M>;
fn add<M: Model<'ctx>>(&mut self, name: &'static str, model: M) -> Self::Out<M> {
let gep_index = self.gep_index_counter;
self.gep_index_counter += 1;
Self::Out { gep_index, name, model }
}
}
/// A traversal to collect the field types of a struct.
///
/// This is used to collect the field types for [`Context::struct_type`].
struct TypeFieldTraversal<'ctx, 'a, G: CodeGenerator + ?Sized> {
generator: &'a G,
ctx: &'ctx Context,
/// The collected field types so far, in order.
field_types: Vec<BasicTypeEnum<'ctx>>,
}
impl<'ctx, 'a, G: CodeGenerator + ?Sized> FieldTraversal<'ctx> for TypeFieldTraversal<'ctx, 'a, G> {
type Out<M> = (); // Checking types return nothing.
fn add<M: Model<'ctx>>(&mut self, _name: &'static str, model: M) -> Self::Out<M> {
let t = model.get_type(self.generator, self.ctx).as_basic_type_enum();
self.field_types.push(t);
}
}
/// A traversal to check the field types of a [`StructType`].
struct CheckTypeFieldTraversal<'ctx, 'a, G: CodeGenerator + ?Sized> {
generator: &'a mut G,
ctx: &'ctx Context,
/// The current GEP index, so we can tell the index of the field we are checking
/// and report the GEP index.
index: u32,
/// The [`StructType`] to check.
scrutinee: StructType<'ctx>,
/// The list of collected errors so far.
errors: Vec<ModelError>,
}
impl<'ctx, 'a, G: CodeGenerator + ?Sized> FieldTraversal<'ctx>
for CheckTypeFieldTraversal<'ctx, 'a, G>
{
type Out<M> = (); // Checking types return nothing.
fn add<M: Model<'ctx>>(&mut self, name: &'static str, model: M) -> Self::Out<M> {
let i = self.index;
self.index += 1;
if let Some(t) = self.scrutinee.get_field_type_at_index(i) {
if let Err(err) = model.check_type(self.generator, self.ctx, t) {
self.errors.push(err.under_context(format!("field #{i} '{name}'").as_str()));
}
} // Otherwise, it will be caught by Struct's `check_type`.
}
}
/// A trait for Rust structs identifying LLVM structures.
///
/// ### Example
///
/// Suppose you want to define this structure:
/// ```c
/// template <typename T>
/// struct ContiguousNDArray {
/// size_t ndims;
/// size_t* shape;
/// T* data;
/// }
/// ```
///
/// This is how it should be done:
/// ```ignore
/// pub struct ContiguousNDArrayFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>> {
/// pub ndims: F::Out<Int<SizeT>>,
/// pub shape: F::Out<Ptr<Int<SizeT>>>,
/// pub data: F::Out<Ptr<Item>>,
/// }
///
/// /// An ndarray without strides and non-opaque `data` field in NAC3.
/// #[derive(Debug, Clone, Copy)]
/// pub struct ContiguousNDArray<M> {
/// /// [`Model`] of the items.
/// pub item: M,
/// }
///
/// impl<'ctx, Item: Model<'ctx>> StructKind<'ctx> for ContiguousNDArray<Item> {
/// type Fields<F: FieldTraversal<'ctx>> = ContiguousNDArrayFields<'ctx, F, Item>;
///
/// fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
/// // The order of `traversal.add*` is important
/// Self::Fields {
/// ndims: traversal.add_auto("ndims"),
/// shape: traversal.add_auto("shape"),
/// data: traversal.add("data", Ptr(self.item)),
/// }
/// }
/// }
/// ```
///
/// The [`FieldTraversal`] here is a mechanism to allow the fields of `ContiguousNDArrayFields` to be
/// traversed to do useful work such as:
///
/// - To create the [`StructType`] of `ContiguousNDArray` by collecting [`BasicType`]s of the fields.
/// - To enable the `.gep(ctx, |f| f.ndims).store(ctx, ...)` syntax.
///
/// Suppose now that you have defined `ContiguousNDArray` and you want to allocate a `ContiguousNDArray`
/// with dtype `float64` in LLVM, this is how you do it:
/// ```rust
/// type F64NDArray = ContiguousNDArray<Float<Float64>>; // Type alias for leaner documentation
/// let model: Struct<F64NDArray> = Struct(ContigousNDArray { item: Float(Float64) });
/// // In fact you may even do `let model = Struct<F64NDArray>::default()`.
/// let ndarray: Instance<'ctx, Ptr<F64NDArray>> = model.alloca(generator, ctx);
/// ```
///
/// ...and here is how you may manipulate/access `ndarray`:
///
/// (NOTE: some arguments have been omitted)
///
/// ```rust
/// // Get `&ndarray->data`
/// ndarray.gep(|f| f.data); // type: Instance<'ctx, Ptr<Float<Float64>>>
///
/// // Get `ndarray->ndims`
/// ndarray.get(|f| f.ndims); // type: Instance<'ctx, Int<SizeT>>
///
/// // Get `&ndarray->ndims`
/// ndarray.gep(|f| f.ndims); // type: Instance<'ctx, Ptr<Int<SizeT>>>
///
/// // Get `ndarray->shape[0]`
/// ndarray.get(|f| f.shape).get_index_const(0); // Instance<'ctx, Int<SizeT>>
///
/// // Get `&ndarray->shape[2]`
/// ndarray.get(|f| f.shape).offset_const(2); // Instance<'ctx, Ptr<Int<SizeT>>>
///
/// // Do `ndarray->ndims = 3;`
/// let num_3 = Int(SizeT).const_int(3);
/// ndarray.set(|f| f.ndims, num_3);
/// ```
pub trait StructKind<'ctx>: fmt::Debug + Clone + Copy {
/// The associated fields of this struct.
type Fields<F: FieldTraversal<'ctx>>;
/// Traverse map through all fields of this [`StructKind`].
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F>;
/// Get a convenience structure to get a struct field's GEP index through its corresponding Rust field.
fn fields(&self) -> Self::Fields<GepFieldTraversal> {
self.traverse_fields(&mut GepFieldTraversal { gep_index_counter: 0 })
}
/// Get the LLVM [`StructType`] of this [`StructKind`].
fn get_struct_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> StructType<'ctx> {
let mut traversal = TypeFieldTraversal { generator, ctx, field_types: Vec::new() };
self.traverse_fields(&mut traversal);
ctx.struct_type(&traversal.field_types, false)
}
}
#[derive(Debug, Clone, Copy, Default)]
pub struct Struct<S>(pub S);
impl<'ctx, S: StructKind<'ctx>> Struct<S> {
/// Create a constant struct value.
///
/// This function also validates `fields` and panics types don't match.
pub fn const_struct<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
fields: &[BasicValueEnum<'ctx>],
) -> Instance<'ctx, Self> {
// NOTE: There *could* have been a functor `F<M> = Instance<'ctx, M>` for `S::Fields<F>`
// to create a more user-friendly interface, but Rust's type system is not sophisticated enough
// and if you try doing that Rust would force you put lifetimes everywhere.
let val = ctx.const_struct(fields, false);
self.check_value(generator, ctx, val).unwrap()
}
}
impl<'ctx, S: StructKind<'ctx>> Model<'ctx> for Struct<S> {
type Value = StructValue<'ctx>;
type Type = StructType<'ctx>;
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
self.0.get_struct_type(generator, ctx)
}
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
ty: T,
) -> Result<(), ModelError> {
let ty = ty.as_basic_type_enum();
let Ok(ty) = StructType::try_from(ty) else {
return Err(ModelError(format!("Expecting StructType, but got {ty:?}")));
};
let mut traversal =
CheckTypeFieldTraversal { generator, ctx, index: 0, errors: Vec::new(), scrutinee: ty };
self.0.traverse_fields(&mut traversal);
let exp_num_fields = traversal.index;
let got_num_fields = u32::try_from(ty.get_field_types().len()).unwrap();
if exp_num_fields != got_num_fields {
return Err(ModelError(format!(
"Expecting StructType with {exp_num_fields} field(s), but got {got_num_fields}"
)));
}
if !traversal.errors.is_empty() {
// Currently, only the first error is reported.
return Err(traversal.errors[0].clone());
}
Ok(())
}
}
impl<'ctx, S: StructKind<'ctx>> Instance<'ctx, Struct<S>> {
/// Get a field with [`StructValue::get_field_at_index`].
pub fn get_field<G: CodeGenerator + ?Sized, M, GetField>(
&self,
generator: &mut G,
ctx: &'ctx Context,
get_field: GetField,
) -> Instance<'ctx, M>
where
M: Model<'ctx>,
GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
{
let field = get_field(self.model.0.fields());
let val = self.value.get_field_at_index(field.gep_index as u32).unwrap();
field.model.check_value(generator, ctx, val).unwrap()
}
}
impl<'ctx, S: StructKind<'ctx>> Instance<'ctx, Ptr<Struct<S>>> {
/// Get a pointer to a field with [`Builder::build_in_bounds_gep`].
pub fn gep<M, GetField>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
get_field: GetField,
) -> Instance<'ctx, Ptr<M>>
where
M: Model<'ctx>,
GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
{
let field = get_field(self.model.0 .0.fields());
let llvm_i32 = ctx.ctx.i32_type();
let ptr = unsafe {
ctx.builder
.build_in_bounds_gep(
self.value,
&[llvm_i32.const_zero(), llvm_i32.const_int(field.gep_index, false)],
field.name,
)
.unwrap()
};
Ptr(field.model).believe_value(ptr)
}
/// Convenience function equivalent to `.gep(...).load(...)`.
pub fn get<M, GetField, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
get_field: GetField,
) -> Instance<'ctx, M>
where
M: Model<'ctx>,
GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
{
self.gep(ctx, get_field).load(generator, ctx)
}
/// Convenience function equivalent to `.gep(...).store(...)`.
pub fn set<M, GetField>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
get_field: GetField,
value: Instance<'ctx, M>,
) where
M: Model<'ctx>,
GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
{
self.gep(ctx, get_field).store(ctx, value);
}
}

40
nac3core/src/codegen/model/util.rs
View File

@ -1,40 +0,0 @@
use crate::codegen::{
stmt::{gen_for_callback_incrementing, BreakContinueHooks},
CodeGenContext, CodeGenerator,
};
use super::*;
/// Like [`gen_for_callback_incrementing`] with [`Model`] abstractions.
pub fn gen_for_model<'ctx, 'a, G, F, N>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, 'a>,
start: Instance<'ctx, Int<N>>,
stop: Instance<'ctx, Int<N>>,
step: Instance<'ctx, Int<N>>,
body: F,
) -> Result<(), String>
where
G: CodeGenerator + ?Sized,
F: FnOnce(
&mut G,
&mut CodeGenContext<'ctx, 'a>,
BreakContinueHooks<'ctx>,
Instance<'ctx, Int<N>>,
) -> Result<(), String>,
N: IntKind<'ctx> + Default,
{
let int_model = Int(N::default());
gen_for_callback_incrementing(
generator,
ctx,
None,
start.value,
(stop.value, false),
|g, ctx, hooks, i| {
let i = int_model.believe_value(i);
body(g, ctx, hooks, i)
},
step.value,
)
}

2434
nac3core/src/codegen/numpy.rs
View File

File diff suppressed because it is too large Load Diff

12
nac3core/src/codegen/object/any.rs
View File

@ -1,12 +0,0 @@
use inkwell::values::BasicValueEnum;
use crate::typecheck::typedef::Type;
/// An NAC3 LLVM Python object.
#[derive(Debug, Clone, Copy)]
pub struct AnyObject<'ctx> {
/// Typechecker type of the object.
pub ty: Type,
/// LLVM value of the object.
pub value: BasicValueEnum<'ctx>,
}

24
nac3core/src/codegen/object/cslice.rs
View File

@ -1,24 +0,0 @@
use crate::codegen::model::*;
/// Fields of [`CSlice`]
pub struct CSliceFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>> {
/// Pointer to items
pub base: F::Out<Ptr<Item>>,
/// Number of items (not bytes)
pub len: F::Out<Int<SizeT>>,
}
/// See <https://docs.rs/cslice/0.3.0/cslice/struct.CSlice.html>.
///
/// Additionally, see <https://github.com/m-labs/artiq/blob/b0d2705c385f64b6e6711c1726cd9178f40b598e/artiq/firmware/libeh/eh_artiq.rs>)
/// for ARTIQ-specific notes.
#[derive(Debug, Clone, Copy, Default)]
pub struct CSlice<Item>(pub Item);
impl<'ctx, Item: Model<'ctx>> StructKind<'ctx> for CSlice<Item> {
type Fields<F: FieldTraversal<'ctx>> = CSliceFields<'ctx, F, Item>;
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
CSliceFields { base: traversal.add("base", Ptr(self.0)), len: traversal.add_auto("len") }
}
}

41
nac3core/src/codegen/object/exception.rs
View File

@ -1,41 +0,0 @@
use crate::codegen::model::*;
use super::str::Str;
/// Fields of [`Exception<'ctx>`]
///
/// The definition came from `pub struct Exception<'a>` in
/// <https://github.com/m-labs/artiq/blob/master/artiq/firmware/libeh/eh_artiq.rs>.
pub struct ExceptionFields<'ctx, F: FieldTraversal<'ctx>> {
pub id: F::Out<Int<Int32>>,
pub filename: F::Out<Str>,
pub line: F::Out<Int<Int32>>,
pub column: F::Out<Int<Int32>>,
pub function: F::Out<Str>,
pub msg: F::Out<Str>,
pub params: [F::Out<Int<Int64>>; 3],
}
/// nac3core & ARTIQ's Exception
#[derive(Debug, Clone, Copy, Default)]
pub struct Exception;
impl<'ctx> StructKind<'ctx> for Exception {
type Fields<F: FieldTraversal<'ctx>> = ExceptionFields<'ctx, F>;
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields {
id: traversal.add_auto("id"),
filename: traversal.add_auto("filename"),
line: traversal.add_auto("line"),
column: traversal.add_auto("column"),
function: traversal.add_auto("function"),
msg: traversal.add_auto("msg"),
params: [
traversal.add_auto("params[0]"),
traversal.add_auto("params[1]"),
traversal.add_auto("params[2]"),
],
}
}
}

107
nac3core/src/codegen/object/list.rs
View File

@ -1,107 +0,0 @@
use crate::{
codegen::{model::*, CodeGenContext, CodeGenerator},
typecheck::typedef::{iter_type_vars, Type, TypeEnum},
};
use super::any::AnyObject;
/// Fields of [`List`]
pub struct ListFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>> {
/// Array pointer to content
pub items: F::Out<Ptr<Item>>,
/// Number of items in the array
pub len: F::Out<Int<SizeT>>,
}
/// A list in NAC3.
#[derive(Debug, Clone, Copy, Default)]
pub struct List<Item> {
/// Model of the list items
pub item: Item,
}
impl<'ctx, Item: Model<'ctx>> StructKind<'ctx> for List<Item> {
type Fields<F: FieldTraversal<'ctx>> = ListFields<'ctx, F, Item>;
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields {
items: traversal.add("items", Ptr(self.item)),
len: traversal.add_auto("len"),
}
}
}
/// A NAC3 Python List object.
#[derive(Debug, Clone, Copy)]
pub struct ListObject<'ctx> {
/// Typechecker type of the list items
pub item_type: Type,
pub instance: Instance<'ctx, Ptr<Struct<List<Any<'ctx>>>>>,
}
impl<'ctx> ListObject<'ctx> {
/// Create a [`ListObject`] from an LLVM value and its typechecker [`Type`].
pub fn from_object<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
object: AnyObject<'ctx>,
) -> Self {
// Check typechecker type and extract `item_type`
let item_type = match &*ctx.unifier.get_ty(object.ty) {
TypeEnum::TObj { obj_id, params, .. }
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
{
iter_type_vars(params).next().unwrap().ty // Extract `item_type`
}
_ => {
panic!("Expecting type to be a list, but got {}", ctx.unifier.stringify(object.ty))
}
};
let plist = Ptr(Struct(List { item: Any(ctx.get_llvm_type(generator, item_type)) }));
// Create object
let value = plist.check_value(generator, ctx.ctx, object.value).unwrap();
ListObject { item_type, instance: value }
}
/// Get the `len()` of this list.
pub fn len<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<SizeT>> {
self.instance.get(generator, ctx, |f| f.len)
}
/// Get the `items` field as an opaque pointer.
pub fn get_opaque_items_ptr<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Ptr<Int<Byte>>> {
self.instance.get(generator, ctx, |f| f.items).pointer_cast(generator, ctx, Int(Byte))
}
/// Get the value of this [`ListObject`] as a list with opaque items.
///
/// This function allocates on the stack to create the list, but the
/// reference to the `items` are preserved.
pub fn get_opaque_list_ptr<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Ptr<Struct<List<Int<Byte>>>>> {
let opaque_list = Struct(List { item: Int(Byte) }).alloca(generator, ctx);
// Copy items pointer
let items = self.get_opaque_items_ptr(generator, ctx);
opaque_list.set(ctx, |f| f.items, items);
// Copy len
let len = self.instance.get(generator, ctx, |f| f.len);
opaque_list.set(ctx, |f| f.len, len);
opaque_list
}
}

9
nac3core/src/codegen/object/mod.rs
View File

@ -1,9 +0,0 @@
pub mod any;
pub mod cslice;
pub mod exception;
pub mod list;
pub mod ndarray;
pub mod range;
pub mod slice;
pub mod str;
pub mod tuple;

178
nac3core/src/codegen/object/ndarray/array.rs
View File

@ -1,178 +0,0 @@
use super::NDArrayObject;
use crate::{
codegen::{
irrt::{
call_nac3_ndarray_array_set_and_validate_list_shape,
call_nac3_ndarray_array_write_list_to_array,
},
model::*,
object::{any::AnyObject, list::ListObject},
stmt::gen_if_else_expr_callback,
CodeGenContext, CodeGenerator,
},
toplevel::helper::{arraylike_flatten_element_type, arraylike_get_ndims},
typecheck::typedef::{Type, TypeEnum},
};
fn get_list_object_dtype_and_ndims<'ctx>(
ctx: &mut CodeGenContext<'ctx, '_>,
list: ListObject<'ctx>,
) -> (Type, u64) {
let dtype = arraylike_flatten_element_type(&mut ctx.unifier, list.item_type);
let ndims = arraylike_get_ndims(&mut ctx.unifier, list.item_type);
let ndims = ndims + 1; // To count `list` itself.
(dtype, ndims)
}
impl<'ctx> NDArrayObject<'ctx> {
fn make_np_array_list_copy_impl<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
list: ListObject<'ctx>,
) -> Self {
let (dtype, ndims_int) = get_list_object_dtype_and_ndims(ctx, list);
let list_value = list.get_opaque_list_ptr(generator, ctx);
// Validate `list` has a consistent shape.
// Raise an exception if `list` is something abnormal like `[[1, 2], [3]]`.
// If `list` has a consistent shape, deduce the shape and write it to `shape`.
let ndims = Int(SizeT).const_int(generator, ctx.ctx, ndims_int);
let shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
call_nac3_ndarray_array_set_and_validate_list_shape(
generator, ctx, list_value, ndims, shape,
);
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims_int);
ndarray.copy_shape_from_array(generator, ctx, shape);
ndarray.create_data(generator, ctx);
// Copy all contents from the list.
call_nac3_ndarray_array_write_list_to_array(generator, ctx, list_value, ndarray.instance);
ndarray
}
fn make_np_array_list_try_no_copy_impl<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
list: ListObject<'ctx>,
) -> Self {
// np_array without copying is only possible `list` is not nested.
//
// If `list` is `list[T]`, we can create an ndarray with `data` set
// to the array pointer of `list`.
//
// If `list` is `list[list[T]]` or worse, copy.
let (dtype, ndims) = get_list_object_dtype_and_ndims(ctx, list);
if ndims == 1 {
// `list` is not nested
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, 1);
// Set data
let data = list.get_opaque_items_ptr(generator, ctx);
ndarray.instance.set(ctx, |f| f.data, data);
// ndarray->shape[0] = list->len;
let shape = ndarray.instance.get(generator, ctx, |f| f.shape);
let list_len = list.instance.get(generator, ctx, |f| f.len);
shape.set_index_const(ctx, 0, list_len);
// Set strides, the `data` is contiguous
ndarray.set_strides_contiguous(generator, ctx);
ndarray
} else {
// `list` is nested, copy
NDArrayObject::make_np_array_list_copy_impl(generator, ctx, list)
}
}
fn make_np_array_list_impl<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
list: ListObject<'ctx>,
copy: Instance<'ctx, Int<Bool>>,
) -> Self {
let (dtype, ndims) = get_list_object_dtype_and_ndims(ctx, list);
let ndarray = gen_if_else_expr_callback(
generator,
ctx,
|_generator, _ctx| Ok(copy.value),
|generator, ctx| {
let ndarray = NDArrayObject::make_np_array_list_copy_impl(generator, ctx, list);
Ok(Some(ndarray.instance.value))
},
|generator, ctx| {
let ndarray =
NDArrayObject::make_np_array_list_try_no_copy_impl(generator, ctx, list);
Ok(Some(ndarray.instance.value))
},
)
.unwrap()
.unwrap();
NDArrayObject::from_value_and_unpacked_types(generator, ctx, ndarray, dtype, ndims)
}
pub fn make_np_array_ndarray_impl<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: NDArrayObject<'ctx>,
copy: Instance<'ctx, Int<Bool>>,
) -> Self {
let ndarray_val = gen_if_else_expr_callback(
generator,
ctx,
|_generator, _ctx| Ok(copy.value),
|generator, ctx| {
let ndarray = ndarray.make_copy(generator, ctx); // Force copy
Ok(Some(ndarray.instance.value))
},
|_generator, _ctx| {
// No need to copy. Return `ndarray` itself.
Ok(Some(ndarray.instance.value))
},
)
.unwrap()
.unwrap();
NDArrayObject::from_value_and_unpacked_types(
generator,
ctx,
ndarray_val,
ndarray.dtype,
ndarray.ndims,
)
}
/// Create a new ndarray like `np.array()`.
///
/// NOTE: The `ndmin` argument is not here. You may want to
/// do [`NDArrayObject::atleast_nd`] to achieve that.
pub fn make_np_array<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
object: AnyObject<'ctx>,
copy: Instance<'ctx, Int<Bool>>,
) -> Self {
match &*ctx.unifier.get_ty(object.ty) {
TypeEnum::TObj { obj_id, .. }
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
{
let list = ListObject::from_object(generator, ctx, object);
NDArrayObject::make_np_array_list_impl(generator, ctx, list, copy)
}
TypeEnum::TObj { obj_id, .. }
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
{
let ndarray = NDArrayObject::from_object(generator, ctx, object);
NDArrayObject::make_np_array_ndarray_impl(generator, ctx, ndarray, copy)
}
_ => panic!("Unrecognized object type: {}", ctx.unifier.stringify(object.ty)), // Typechecker ensures this
}
}
}

135
nac3core/src/codegen/object/ndarray/broadcast.rs
View File

@ -1,135 +0,0 @@
use itertools::Itertools;
use crate::codegen::{
irrt::{call_nac3_ndarray_broadcast_shapes, call_nac3_ndarray_broadcast_to},
model::*,
CodeGenContext, CodeGenerator,
};
use super::NDArrayObject;
/// Fields of [`ShapeEntry`]
pub struct ShapeEntryFields<'ctx, F: FieldTraversal<'ctx>> {
pub ndims: F::Out<Int<SizeT>>,
pub shape: F::Out<Ptr<Int<SizeT>>>,
}
/// An IRRT structure used in broadcasting.
#[derive(Debug, Clone, Copy, Default)]
pub struct ShapeEntry;
impl<'ctx> StructKind<'ctx> for ShapeEntry {
type Fields<F: FieldTraversal<'ctx>> = ShapeEntryFields<'ctx, F>;
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields { ndims: traversal.add_auto("ndims"), shape: traversal.add_auto("shape") }
}
}
impl<'ctx> NDArrayObject<'ctx> {
/// Create a broadcast view on this ndarray with a target shape.
///
/// The input shape will be checked to make sure that it contains no negative values.
///
/// * `target_ndims` - The ndims type after broadcasting to the given shape.
/// The caller has to figure this out for this function.
/// * `target_shape` - An array pointer pointing to the target shape.
#[must_use]
pub fn broadcast_to<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
target_ndims: u64,
target_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> Self {
let broadcast_ndarray = NDArrayObject::alloca(generator, ctx, self.dtype, target_ndims);
broadcast_ndarray.copy_shape_from_array(generator, ctx, target_shape);
call_nac3_ndarray_broadcast_to(generator, ctx, self.instance, broadcast_ndarray.instance);
broadcast_ndarray
}
}
/// A result produced by [`broadcast_all_ndarrays`]
#[derive(Debug, Clone)]
pub struct BroadcastAllResult<'ctx> {
/// The statically known `ndims` of the broadcast result.
pub ndims: u64,
/// The broadcasting shape.
pub shape: Instance<'ctx, Ptr<Int<SizeT>>>,
/// Broadcasted views on the inputs.
///
/// All of them will have `shape` [`BroadcastAllResult::shape`] and
/// `ndims` [`BroadcastAllResult::ndims`]. The length of the vector
/// is the same as the input.
pub ndarrays: Vec<NDArrayObject<'ctx>>,
}
/// Helper function to call `call_nac3_ndarray_broadcast_shapes`
fn broadcast_shapes<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
in_shape_entries: &[(Instance<'ctx, Ptr<Int<SizeT>>>, u64)], // (shape, shape's length/ndims)
broadcast_ndims: u64,
broadcast_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
// Prepare input shape entries to be passed to `call_nac3_ndarray_broadcast_shapes`.
let num_shape_entries =
Int(SizeT).const_int(generator, ctx.ctx, u64::try_from(in_shape_entries.len()).unwrap());
let shape_entries = Struct(ShapeEntry).array_alloca(generator, ctx, num_shape_entries.value);
for (i, (in_shape, in_ndims)) in in_shape_entries.iter().enumerate() {
let pshape_entry = shape_entries.offset_const(ctx, i as u64);
let in_ndims = Int(SizeT).const_int(generator, ctx.ctx, *in_ndims);
pshape_entry.set(ctx, |f| f.ndims, in_ndims);
pshape_entry.set(ctx, |f| f.shape, *in_shape);
}
let broadcast_ndims = Int(SizeT).const_int(generator, ctx.ctx, broadcast_ndims);
call_nac3_ndarray_broadcast_shapes(
generator,
ctx,
num_shape_entries,
shape_entries,
broadcast_ndims,
broadcast_shape,
);
}
impl<'ctx> NDArrayObject<'ctx> {
/// Broadcast all ndarrays according to `np.broadcast()` and return a [`BroadcastAllResult`]
/// containing all the information of the result of the broadcast operation.
pub fn broadcast<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarrays: &[Self],
) -> BroadcastAllResult<'ctx> {
assert!(!ndarrays.is_empty());
// Infer the broadcast output ndims.
let broadcast_ndims_int = ndarrays.iter().map(|ndarray| ndarray.ndims).max().unwrap();
let broadcast_ndims = Int(SizeT).const_int(generator, ctx.ctx, broadcast_ndims_int);
let broadcast_shape = Int(SizeT).array_alloca(generator, ctx, broadcast_ndims.value);
let shape_entries = ndarrays
.iter()
.map(|ndarray| (ndarray.instance.get(generator, ctx, |f| f.shape), ndarray.ndims))
.collect_vec();
broadcast_shapes(generator, ctx, &shape_entries, broadcast_ndims_int, broadcast_shape);
// Broadcast all the inputs to shape `dst_shape`.
let broadcast_ndarrays: Vec<_> = ndarrays
.iter()
.map(|ndarray| {
ndarray.broadcast_to(generator, ctx, broadcast_ndims_int, broadcast_shape)
})
.collect_vec();
BroadcastAllResult {
ndims: broadcast_ndims_int,
shape: broadcast_shape,
ndarrays: broadcast_ndarrays,
}
}
}

134
nac3core/src/codegen/object/ndarray/contiguous.rs
View File

@ -1,134 +0,0 @@
use crate::{
codegen::{model::*, CodeGenContext, CodeGenerator},
typecheck::typedef::Type,
};
use super::NDArrayObject;
/// Fields of [`ContiguousNDArray`]
pub struct ContiguousNDArrayFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>> {
pub ndims: F::Out<Int<SizeT>>,
pub shape: F::Out<Ptr<Int<SizeT>>>,
pub data: F::Out<Ptr<Item>>,
}
/// An ndarray without strides and non-opaque `data` field in NAC3.
#[derive(Debug, Clone, Copy)]
pub struct ContiguousNDArray<M> {
/// [`Model`] of the items.
pub item: M,
}
impl<'ctx, Item: Model<'ctx>> StructKind<'ctx> for ContiguousNDArray<Item> {
type Fields<F: FieldTraversal<'ctx>> = ContiguousNDArrayFields<'ctx, F, Item>;
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields {
ndims: traversal.add_auto("ndims"),
shape: traversal.add_auto("shape"),
data: traversal.add("data", Ptr(self.item)),
}
}
}
impl<'ctx> NDArrayObject<'ctx> {
/// Create a [`ContiguousNDArray`] from the contents of this ndarray.
///
/// This function may or may not be expensive depending on if this ndarray has contiguous data.
///
/// If this ndarray is not C-contiguous, this function will allocate memory on the stack for the `data` field of
/// the returned [`ContiguousNDArray`] and copy contents of this ndarray to there.
///
/// If this ndarray is C-contiguous, contents of this ndarray will not be copied. The created [`ContiguousNDArray`]
/// will share memory with this ndarray.
///
/// The `item_model` sets the [`Model`] of the returned [`ContiguousNDArray`]'s `Item` model for type-safety, and
/// should match the `ctx.get_llvm_type()` of this ndarray's `dtype`. Otherwise this function panics. Use model [`Any`]
/// if you don't care/cannot know the [`Model`] in advance.
pub fn make_contiguous_ndarray<G: CodeGenerator + ?Sized, Item: Model<'ctx>>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
item_model: Item,
) -> Instance<'ctx, Ptr<Struct<ContiguousNDArray<Item>>>> {
// Sanity check on `self.dtype` and `item_model`.
let dtype_llvm = ctx.get_llvm_type(generator, self.dtype);
item_model.check_type(generator, ctx.ctx, dtype_llvm).unwrap();
let cdarray_model = Struct(ContiguousNDArray { item: item_model });
let current_bb = ctx.builder.get_insert_block().unwrap();
let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then_bb");
let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "else_bb");
let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "end_bb");
// Allocate and setup the resulting [`ContiguousNDArray`].
let result = cdarray_model.alloca(generator, ctx);
// Set ndims and shape.
let ndims = self.ndims_llvm(generator, ctx.ctx);
result.set(ctx, |f| f.ndims, ndims);
let shape = self.instance.get(generator, ctx, |f| f.shape);
result.set(ctx, |f| f.shape, shape);
let is_contiguous = self.is_c_contiguous(generator, ctx);
ctx.builder.build_conditional_branch(is_contiguous.value, then_bb, else_bb).unwrap();
// Inserting into then_bb; This ndarray is contiguous.
ctx.builder.position_at_end(then_bb);
let data = self.instance.get(generator, ctx, |f| f.data);
let data = data.pointer_cast(generator, ctx, item_model);
result.set(ctx, |f| f.data, data);
ctx.builder.build_unconditional_branch(end_bb).unwrap();
// Inserting into else_bb; This ndarray is not contiguous. Do a full-copy on `data`.
// `make_copy` produces an ndarray with contiguous `data`.
ctx.builder.position_at_end(else_bb);
let copied_ndarray = self.make_copy(generator, ctx);
let data = copied_ndarray.instance.get(generator, ctx, |f| f.data);
let data = data.pointer_cast(generator, ctx, item_model);
result.set(ctx, |f| f.data, data);
ctx.builder.build_unconditional_branch(end_bb).unwrap();
// Reposition to end_bb for continuation
ctx.builder.position_at_end(end_bb);
result
}
/// Create an [`NDArrayObject`] from a [`ContiguousNDArray`].
///
/// The operation is super cheap. The newly created [`NDArrayObject`] will share the
/// same memory as the [`ContiguousNDArray`].
///
/// `ndims` has to be provided as [`NDArrayObject`] requires a statically known `ndims` value, despite
/// the fact that the information should be contained within the [`ContiguousNDArray`].
pub fn from_contiguous_ndarray<G: CodeGenerator + ?Sized, Item: Model<'ctx>>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
carray: Instance<'ctx, Ptr<Struct<ContiguousNDArray<Item>>>>,
dtype: Type,
ndims: u64,
) -> Self {
// Sanity check on `dtype` and `contiguous_array`'s `Item` model.
let dtype_llvm = ctx.get_llvm_type(generator, dtype);
carray.model.0 .0.item.check_type(generator, ctx.ctx, dtype_llvm).unwrap();
// TODO: Debug assert `ndims == carray.ndims` to catch bugs.
// Allocate the resulting ndarray.
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims);
// Copy shape and update strides
let shape = carray.get(generator, ctx, |f| f.shape);
ndarray.copy_shape_from_array(generator, ctx, shape);
ndarray.set_strides_contiguous(generator, ctx);
// Share data
let data = carray.get(generator, ctx, |f| f.data).pointer_cast(generator, ctx, Int(Byte));
ndarray.instance.set(ctx, |f| f.data, data);
ndarray
}
}

176
nac3core/src/codegen/object/ndarray/factory.rs
View File

@ -1,176 +0,0 @@
use inkwell::{values::BasicValueEnum, IntPredicate};
use crate::{
codegen::{
irrt::call_nac3_ndarray_util_assert_shape_no_negative, model::*, CodeGenContext,
CodeGenerator,
},
typecheck::typedef::Type,
};
use super::NDArrayObject;
/// Get the zero value in `np.zeros()` of a `dtype`.
fn ndarray_zero_value<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
) -> BasicValueEnum<'ctx> {
if [ctx.primitives.int32, ctx.primitives.uint32]
.iter()
.any(|ty| ctx.unifier.unioned(dtype, *ty))
{
ctx.ctx.i32_type().const_zero().into()
} else if [ctx.primitives.int64, ctx.primitives.uint64]
.iter()
.any(|ty| ctx.unifier.unioned(dtype, *ty))
{
ctx.ctx.i64_type().const_zero().into()
} else if ctx.unifier.unioned(dtype, ctx.primitives.float) {
ctx.ctx.f64_type().const_zero().into()
} else if ctx.unifier.unioned(dtype, ctx.primitives.bool) {
ctx.ctx.bool_type().const_zero().into()
} else if ctx.unifier.unioned(dtype, ctx.primitives.str) {
ctx.gen_string(generator, "").value.into()
} else {
panic!("unrecognized dtype: {}", ctx.unifier.stringify(dtype));
}
}
/// Get the one value in `np.ones()` of a `dtype`.
fn ndarray_one_value<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
) -> BasicValueEnum<'ctx> {
if [ctx.primitives.int32, ctx.primitives.uint32]
.iter()
.any(|ty| ctx.unifier.unioned(dtype, *ty))
{
let is_signed = ctx.unifier.unioned(dtype, ctx.primitives.int32);
ctx.ctx.i32_type().const_int(1, is_signed).into()
} else if [ctx.primitives.int64, ctx.primitives.uint64]
.iter()
.any(|ty| ctx.unifier.unioned(dtype, *ty))
{
let is_signed = ctx.unifier.unioned(dtype, ctx.primitives.int64);
ctx.ctx.i64_type().const_int(1, is_signed).into()
} else if ctx.unifier.unioned(dtype, ctx.primitives.float) {
ctx.ctx.f64_type().const_float(1.0).into()
} else if ctx.unifier.unioned(dtype, ctx.primitives.bool) {
ctx.ctx.bool_type().const_int(1, false).into()
} else if ctx.unifier.unioned(dtype, ctx.primitives.str) {
ctx.gen_string(generator, "1").value.into()
} else {
panic!("unrecognized dtype: {}", ctx.unifier.stringify(dtype));
}
}
impl<'ctx> NDArrayObject<'ctx> {
/// Create an ndarray like `np.empty`.
pub fn make_np_empty<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
ndims: u64,
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> Self {
// Validate `shape`
let ndims_llvm = Int(SizeT).const_int(generator, ctx.ctx, ndims);
call_nac3_ndarray_util_assert_shape_no_negative(generator, ctx, ndims_llvm, shape);
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims);
ndarray.copy_shape_from_array(generator, ctx, shape);
ndarray.create_data(generator, ctx);
ndarray
}
/// Create an ndarray like `np.full`.
pub fn make_np_full<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
ndims: u64,
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
fill_value: BasicValueEnum<'ctx>,
) -> Self {
let ndarray = NDArrayObject::make_np_empty(generator, ctx, dtype, ndims, shape);
ndarray.fill(generator, ctx, fill_value);
ndarray
}
/// Create an ndarray like `np.zero`.
pub fn make_np_zeros<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
ndims: u64,
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> Self {
let fill_value = ndarray_zero_value(generator, ctx, dtype);
NDArrayObject::make_np_full(generator, ctx, dtype, ndims, shape, fill_value)
}
/// Create an ndarray like `np.ones`.
pub fn make_np_ones<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
ndims: u64,
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> Self {
let fill_value = ndarray_one_value(generator, ctx, dtype);
NDArrayObject::make_np_full(generator, ctx, dtype, ndims, shape, fill_value)
}
/// Create an ndarray like `np.eye`.
pub fn make_np_eye<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
nrows: Instance<'ctx, Int<SizeT>>,
ncols: Instance<'ctx, Int<SizeT>>,
offset: Instance<'ctx, Int<SizeT>>,
) -> Self {
let ndzero = ndarray_zero_value(generator, ctx, dtype);
let ndone = ndarray_one_value(generator, ctx, dtype);
let ndarray = NDArrayObject::alloca_dynamic_shape(generator, ctx, dtype, &[nrows, ncols]);
// Create data and make the matrix like look np.eye()
ndarray.create_data(generator, ctx);
ndarray
.foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
// NOTE: rows and cols can never be zero here, since this ndarray's `np.size` would be zero
// and this loop would not execute.
// Load up `row_i` and `col_i` from indices.
let row_i = nditer.get_indices().get_index_const(generator, ctx, 0);
let col_i = nditer.get_indices().get_index_const(generator, ctx, 1);
let be_one = row_i.add(ctx, offset).compare(ctx, IntPredicate::EQ, col_i);
let value = ctx.builder.build_select(be_one.value, ndone, ndzero, "value").unwrap();
let p = nditer.get_pointer(generator, ctx);
ctx.builder.build_store(p, value).unwrap();
Ok(())
})
.unwrap();
ndarray
}
/// Create an ndarray like `np.identity`.
pub fn make_np_identity<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
size: Instance<'ctx, Int<SizeT>>,
) -> Self {
// Convenient implementation
let offset = Int(SizeT).const_0(generator, ctx.ctx);
NDArrayObject::make_np_eye(generator, ctx, dtype, size, size, offset)
}
}

223
nac3core/src/codegen/object/ndarray/indexing.rs
View File

@ -1,223 +0,0 @@
use crate::codegen::{
irrt::call_nac3_ndarray_index,
model::*,
object::slice::{RustSlice, Slice},
CodeGenContext, CodeGenerator,
};
use super::NDArrayObject;
pub type NDIndexType = Byte;
/// Fields of [`NDIndex`]
#[derive(Debug, Clone, Copy)]
pub struct NDIndexFields<'ctx, F: FieldTraversal<'ctx>> {
pub type_: F::Out<Int<NDIndexType>>, // Defined to be uint8_t in IRRT
pub data: F::Out<Ptr<Int<Byte>>>,
}
/// An IRRT representation of an ndarray subscript index.
#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
pub struct NDIndex;
impl<'ctx> StructKind<'ctx> for NDIndex {
type Fields<F: FieldTraversal<'ctx>> = NDIndexFields<'ctx, F>;
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields { type_: traversal.add_auto("type"), data: traversal.add_auto("data") }
}
}
// A convenience enum to prepare an [`NDIndex`].
#[derive(Debug, Clone)]
pub enum RustNDIndex<'ctx> {
SingleElement(Instance<'ctx, Int<Int32>>), // TODO: To be SizeT
Slice(RustSlice<'ctx, Int32>), // TODO: To be SizeT
NewAxis,
Ellipsis,
}
impl<'ctx> RustNDIndex<'ctx> {
/// Get the value to set `NDIndex::type` for this variant.
fn get_type_id(&self) -> u64 {
// Defined in IRRT, must be in sync
match self {
RustNDIndex::SingleElement(_) => 0,
RustNDIndex::Slice(_) => 1,
RustNDIndex::NewAxis => 2,
RustNDIndex::Ellipsis => 3,
}
}
/// Write the contents to an LLVM [`NDIndex`].
fn write_to_ndindex<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
dst_ndindex_ptr: Instance<'ctx, Ptr<Struct<NDIndex>>>,
) {
// Set `dst_ndindex_ptr->type`
dst_ndindex_ptr.gep(ctx, |f| f.type_).store(
ctx,
Int(NDIndexType::default()).const_int(generator, ctx.ctx, self.get_type_id()),
);
// Set `dst_ndindex_ptr->data`
match self {
RustNDIndex::SingleElement(in_index) => {
let index_ptr = Int(Int32).alloca(generator, ctx);
index_ptr.store(ctx, *in_index);
dst_ndindex_ptr
.gep(ctx, |f| f.data)
.store(ctx, index_ptr.pointer_cast(generator, ctx, Int(Byte)));
}
RustNDIndex::Slice(in_rust_slice) => {
let user_slice_ptr = Struct(Slice(Int32)).alloca(generator, ctx);
in_rust_slice.write_to_slice(generator, ctx, user_slice_ptr);
dst_ndindex_ptr
.gep(ctx, |f| f.data)
.store(ctx, user_slice_ptr.pointer_cast(generator, ctx, Int(Byte)));
}
RustNDIndex::NewAxis | RustNDIndex::Ellipsis => {}
}
}
/// Allocate an array of `NDIndex`es on the stack and return its stack pointer.
pub fn alloca_ndindices<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
in_ndindices: &[RustNDIndex<'ctx>],
) -> (Instance<'ctx, Int<SizeT>>, Instance<'ctx, Ptr<Struct<NDIndex>>>) {
let ndindex_model = Struct(NDIndex);
let num_ndindices = Int(SizeT).const_int(generator, ctx.ctx, in_ndindices.len() as u64);
let ndindices = ndindex_model.array_alloca(generator, ctx, num_ndindices.value);
for (i, in_ndindex) in in_ndindices.iter().enumerate() {
let pndindex = ndindices.offset_const(ctx, i as u64);
in_ndindex.write_to_ndindex(generator, ctx, pndindex);
}
(num_ndindices, ndindices)
}
}
impl<'ctx> NDArrayObject<'ctx> {
/// Get the ndims [`Type`] after indexing with a given slice.
#[must_use]
pub fn deduce_ndims_after_indexing_with(&self, indices: &[RustNDIndex<'ctx>]) -> u64 {
let mut ndims = self.ndims;
for index in indices {
match index {
RustNDIndex::SingleElement(_) => {
ndims -= 1; // Single elements decrements ndims
}
RustNDIndex::NewAxis => {
ndims += 1; // `np.newaxis` / `none` adds a new axis
}
RustNDIndex::Ellipsis | RustNDIndex::Slice(_) => {}
}
}
ndims
}
/// Index into the ndarray, and return a newly-allocated view on this ndarray.
///
/// This function behaves like NumPy's ndarray indexing, but if the indices index
/// into a single element, an unsized ndarray is returned.
#[must_use]
pub fn index<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
indices: &[RustNDIndex<'ctx>],
) -> Self {
let dst_ndims = self.deduce_ndims_after_indexing_with(indices);
let dst_ndarray = NDArrayObject::alloca(generator, ctx, self.dtype, dst_ndims);
let (num_indices, indices) = RustNDIndex::alloca_ndindices(generator, ctx, indices);
call_nac3_ndarray_index(
generator,
ctx,
num_indices,
indices,
self.instance,
dst_ndarray.instance,
);
dst_ndarray
}
}
pub mod util {
use itertools::Itertools;
use nac3parser::ast::{Expr, ExprKind};
use crate::{
codegen::{model::*, object::slice::util::gen_slice, CodeGenContext, CodeGenerator},
typecheck::typedef::Type,
};
use super::RustNDIndex;
/// Generate LLVM code to transform an ndarray subscript expression to
/// its list of [`RustNDIndex`]
///
/// i.e.,
/// ```python
/// my_ndarray[::3, 1, :2:]
/// ^^^^^^^^^^^ Then these into a three `RustNDIndex`es
/// ```
pub fn gen_ndarray_subscript_ndindices<'ctx, G: CodeGenerator>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
subscript: &Expr<Option<Type>>,
) -> Result<Vec<RustNDIndex<'ctx>>, String> {
// TODO: Support https://numpy.org/doc/stable/user/basics.indexing.html#dimensional-indexing-tools
// Annoying notes about `slice`
// - `my_array[5]`
// - slice is a `Constant`
// - `my_array[:5]`
// - slice is a `Slice`
// - `my_array[:]`
// - slice is a `Slice`, but lower upper step would all be `Option::None`
// - `my_array[:, :]`
// - slice is now a `Tuple` of two `Slice`-s
//
// In summary:
// - when there is a comma "," within [], `slice` will be a `Tuple` of the entries.
// - when there is not comma "," within [] (i.e., just a single entry), `slice` will be that entry itself.
//
// So we first "flatten" out the slice expression
let index_exprs = match &subscript.node {
ExprKind::Tuple { elts, .. } => elts.iter().collect_vec(),
_ => vec![subscript],
};
// Process all index expressions
let mut rust_ndindices: Vec<RustNDIndex> = Vec::with_capacity(index_exprs.len()); // Not using iterators here because `?` is used here.
for index_expr in index_exprs {
// NOTE: Currently nac3core's slices do not have an object representation,
// so the code/implementation looks awkward - we have to do pattern matching on the expression
let ndindex = if let ExprKind::Slice { lower, upper, step } = &index_expr.node {
// Handle slices
let slice = gen_slice(generator, ctx, lower, upper, step)?;
RustNDIndex::Slice(slice)
} else {
// Treat and handle everything else as a single element index.
let index = generator.gen_expr(ctx, index_expr)?.unwrap().to_basic_value_enum(
ctx,
generator,
ctx.primitives.int32, // Must be int32, this checks for illegal values
)?;
let index = Int(Int32).check_value(generator, ctx.ctx, index).unwrap();
RustNDIndex::SingleElement(index)
};
rust_ndindices.push(ndindex);
}
Ok(rust_ndindices)
}
}

220
nac3core/src/codegen/object/ndarray/map.rs
View File

@ -1,220 +0,0 @@
use inkwell::values::BasicValueEnum;
use itertools::Itertools;
use crate::{
codegen::{
object::ndarray::{AnyObject, NDArrayObject},
stmt::gen_for_callback,
CodeGenContext, CodeGenerator,
},
typecheck::typedef::Type,
};
use super::{nditer::NDIterHandle, NDArrayOut, ScalarOrNDArray};
impl<'ctx> NDArrayObject<'ctx> {
/// Generate LLVM IR to broadcast `ndarray`s together, and starmap through them with `mapping` elementwise.
///
/// `mapping` is an LLVM IR generator. The input of `mapping` is the list of elements when iterating through
/// the input `ndarrays` after broadcasting. The output of `mapping` is the result of the elementwise operation.
///
/// `out` specifies whether the result should be a new ndarray or to be written an existing ndarray.
pub fn broadcast_starmap<'a, G, MappingFn>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, 'a>,
ndarrays: &[Self],
out: NDArrayOut<'ctx>,
mapping: MappingFn,
) -> Result<Self, String>
where
G: CodeGenerator + ?Sized,
MappingFn: FnOnce(
&mut G,
&mut CodeGenContext<'ctx, 'a>,
&[BasicValueEnum<'ctx>],
) -> Result<BasicValueEnum<'ctx>, String>,
{
// Broadcast inputs
let broadcast_result = NDArrayObject::broadcast(generator, ctx, ndarrays);
let out_ndarray = match out {
NDArrayOut::NewNDArray { dtype } => {
// Create a new ndarray based on the broadcast shape.
let result_ndarray =
NDArrayObject::alloca(generator, ctx, dtype, broadcast_result.ndims);
result_ndarray.copy_shape_from_array(generator, ctx, broadcast_result.shape);
result_ndarray.create_data(generator, ctx);
result_ndarray
}
NDArrayOut::WriteToNDArray { ndarray: result_ndarray } => {
// Use an existing ndarray.
// Check that its shape is compatible with the broadcast shape.
result_ndarray.assert_can_be_written_by_out(
generator,
ctx,
broadcast_result.ndims,
broadcast_result.shape,
);
result_ndarray
}
};
// Map element-wise and store results into `mapped_ndarray`.
let nditer = NDIterHandle::new(generator, ctx, out_ndarray);
gen_for_callback(
generator,
ctx,
Some("broadcast_starmap"),
|generator, ctx| {
// Create NDIters for all broadcasted input ndarrays.
let other_nditers = broadcast_result
.ndarrays
.iter()
.map(|ndarray| NDIterHandle::new(generator, ctx, *ndarray))
.collect_vec();
Ok((nditer, other_nditers))
},
|generator, ctx, (out_nditer, _in_nditers)| {
// We can simply use `out_nditer`'s `has_next()`.
// `in_nditers`' `has_next()`s should return the same value.
Ok(out_nditer.has_next(generator, ctx).value)
},
|generator, ctx, _hooks, (out_nditer, in_nditers)| {
// Get all the scalars from the broadcasted input ndarrays, pass them to `mapping`,
// and write to `out_ndarray`.
let in_scalars = in_nditers
.iter()
.map(|nditer| nditer.get_scalar(generator, ctx).value)
.collect_vec();
let result = mapping(generator, ctx, &in_scalars)?;
let p = out_nditer.get_pointer(generator, ctx);
ctx.builder.build_store(p, result).unwrap();
Ok(())
},
|generator, ctx, (out_nditer, in_nditers)| {
// Advance all iterators
out_nditer.next(generator, ctx);
in_nditers.iter().for_each(|nditer| nditer.next(generator, ctx));
Ok(())
},
)?;
Ok(out_ndarray)
}
/// Map through this ndarray with an elementwise function.
pub fn map<'a, G, Mapping>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, 'a>,
out: NDArrayOut<'ctx>,
mapping: Mapping,
) -> Result<Self, String>
where
G: CodeGenerator + ?Sized,
Mapping: FnOnce(
&mut G,
&mut CodeGenContext<'ctx, 'a>,
BasicValueEnum<'ctx>,
) -> Result<BasicValueEnum<'ctx>, String>,
{
NDArrayObject::broadcast_starmap(
generator,
ctx,
&[*self],
out,
|generator, ctx, scalars| mapping(generator, ctx, scalars[0]),
)
}
}
impl<'ctx> ScalarOrNDArray<'ctx> {
/// Starmap through a list of inputs using `mapping`, where an input could be an ndarray, a scalar.
///
/// This function is very helpful when implementing NumPy functions that takes on either scalars or ndarrays or a mix of them
/// as their inputs and produces either an ndarray with broadcast, or a scalar if all its inputs are all scalars.
///
/// For example ,this function can be used to implement `np.add`, which has the following behaviors:
/// - `np.add(3, 4) = 7` # (scalar, scalar) -> scalar
/// - `np.add(3, np.array([4, 5, 6]))` # (scalar, ndarray) -> ndarray; the first `scalar` is converted into an ndarray and broadcasted.
/// - `np.add(np.array([[1], [2], [3]]), np.array([[4, 5, 6]]))` # (ndarray, ndarray) -> ndarray; there is broadcasting.
///
/// ## Details:
///
/// If `inputs` are all [`ScalarOrNDArray::Scalar`], the output will be a [`ScalarOrNDArray::Scalar`] with type `ret_dtype`.
///
/// Otherwise (if there are any [`ScalarOrNDArray::NDArray`] in `inputs`), all inputs will be 'as-ndarray'-ed into ndarrays,
/// then all inputs (now all ndarrays) will be passed to [`NDArrayObject::broadcasting_starmap`] and **create** a new ndarray
/// with dtype `ret_dtype`.
pub fn broadcasting_starmap<'a, G, MappingFn>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, 'a>,
inputs: &[ScalarOrNDArray<'ctx>],
ret_dtype: Type,
mapping: MappingFn,
) -> Result<ScalarOrNDArray<'ctx>, String>
where
G: CodeGenerator + ?Sized,
MappingFn: FnOnce(
&mut G,
&mut CodeGenContext<'ctx, 'a>,
&[BasicValueEnum<'ctx>],
) -> Result<BasicValueEnum<'ctx>, String>,
{
// Check if all inputs are Scalars
let all_scalars: Option<Vec<_>> = inputs.iter().map(AnyObject::try_from).try_collect().ok();
if let Some(scalars) = all_scalars {
let scalars = scalars.iter().map(|scalar| scalar.value).collect_vec();
let value = mapping(generator, ctx, &scalars)?;
Ok(ScalarOrNDArray::Scalar(AnyObject { ty: ret_dtype, value }))
} else {
// Promote all input to ndarrays and map through them.
let inputs = inputs.iter().map(|input| input.to_ndarray(generator, ctx)).collect_vec();
let ndarray = NDArrayObject::broadcast_starmap(
generator,
ctx,
&inputs,
NDArrayOut::NewNDArray { dtype: ret_dtype },
mapping,
)?;
Ok(ScalarOrNDArray::NDArray(ndarray))
}
}
/// Map through this [`ScalarOrNDArray`] with an elementwise function.
///
/// If this is a scalar, `mapping` will directly act on the scalar. This function will return a [`ScalarOrNDArray::Scalar`] of that result.
///
/// If this is an ndarray, `mapping` will be applied to the elements of the ndarray. A new ndarray of the results will be created and
/// returned as a [`ScalarOrNDArray::NDArray`].
pub fn map<'a, G, Mapping>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, 'a>,
ret_dtype: Type,
mapping: Mapping,
) -> Result<ScalarOrNDArray<'ctx>, String>
where
G: CodeGenerator + ?Sized,
Mapping: FnOnce(
&mut G,
&mut CodeGenContext<'ctx, 'a>,
BasicValueEnum<'ctx>,
) -> Result<BasicValueEnum<'ctx>, String>,
{
ScalarOrNDArray::broadcasting_starmap(
generator,
ctx,
&[*self],
ret_dtype,
|generator, ctx, scalars| mapping(generator, ctx, scalars[0]),
)
}
}

218
nac3core/src/codegen/object/ndarray/matmul.rs
View File

@ -1,218 +0,0 @@
use std::cmp::max;
use nac3parser::ast::Operator;
use util::gen_for_model;
use crate::{
codegen::{
expr::gen_binop_expr_with_values, irrt::call_nac3_ndarray_matmul_calculate_shapes,
model::*, object::ndarray::indexing::RustNDIndex, CodeGenContext, CodeGenerator,
},
typecheck::{magic_methods::Binop, typedef::Type},
};
use super::{NDArrayObject, NDArrayOut};
/// Perform `np.einsum("...ij,...jk->...ik", in_a, in_b)`.
///
/// `dst_dtype` defines the dtype of the returned ndarray.
fn matmul_at_least_2d<'ctx, G: CodeGenerator>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dst_dtype: Type,
in_a: NDArrayObject<'ctx>,
in_b: NDArrayObject<'ctx>,
) -> NDArrayObject<'ctx> {
assert!(in_a.ndims >= 2);
assert!(in_b.ndims >= 2);
// Deduce ndims of the result of matmul.
let ndims_int = max(in_a.ndims, in_b.ndims);
let ndims = Int(SizeT).const_int(generator, ctx.ctx, ndims_int);
let num_0 = Int(SizeT).const_int(generator, ctx.ctx, 0);
let num_1 = Int(SizeT).const_int(generator, ctx.ctx, 1);
// Broadcasts `in_a.shape[:-2]` and `in_b.shape[:-2]` together and allocate the
// destination ndarray to store the result of matmul.
let (lhs, rhs, dst) = {
let in_lhs_ndims = in_a.ndims_llvm(generator, ctx.ctx);
let in_lhs_shape = in_a.instance.get(generator, ctx, |f| f.shape);
let in_rhs_ndims = in_b.ndims_llvm(generator, ctx.ctx);
let in_rhs_shape = in_b.instance.get(generator, ctx, |f| f.shape);
let lhs_shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
let rhs_shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
let dst_shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
// Matmul dimension compatibility is checked here.
call_nac3_ndarray_matmul_calculate_shapes(
generator,
ctx,
in_lhs_ndims,
in_lhs_shape,
in_rhs_ndims,
in_rhs_shape,
ndims,
lhs_shape,
rhs_shape,
dst_shape,
);
let lhs = in_a.broadcast_to(generator, ctx, ndims_int, lhs_shape);
let rhs = in_b.broadcast_to(generator, ctx, ndims_int, rhs_shape);
let dst = NDArrayObject::alloca(generator, ctx, dst_dtype, ndims_int);
dst.copy_shape_from_array(generator, ctx, dst_shape);
dst.create_data(generator, ctx);
(lhs, rhs, dst)
};
let len = lhs.instance.get(generator, ctx, |f| f.shape).get_index_const(
generator,
ctx,
ndims_int - 1,
);
let at_row = ndims_int - 2;
let at_col = ndims_int - 1;
let dst_dtype_llvm = ctx.get_llvm_type(generator, dst_dtype);
let dst_zero = dst_dtype_llvm.const_zero();
dst.foreach(generator, ctx, |generator, ctx, _, hdl| {
let pdst_ij = hdl.get_pointer(generator, ctx);
ctx.builder.build_store(pdst_ij, dst_zero).unwrap();
let indices = hdl.get_indices();
let i = indices.get_index_const(generator, ctx, at_row);
let j = indices.get_index_const(generator, ctx, at_col);
gen_for_model(generator, ctx, num_0, len, num_1, |generator, ctx, _, k| {
// `indices` is modified to index into `a` and `b`, and restored.
indices.set_index_const(ctx, at_row, i);
indices.set_index_const(ctx, at_col, k);
let a_ik = lhs.get_scalar_by_indices(generator, ctx, indices);
indices.set_index_const(ctx, at_row, k);
indices.set_index_const(ctx, at_col, j);
let b_kj = rhs.get_scalar_by_indices(generator, ctx, indices);
// Restore `indices`.
indices.set_index_const(ctx, at_row, i);
indices.set_index_const(ctx, at_col, j);
// x = a_[...]ik * b_[...]kj
let x = gen_binop_expr_with_values(
generator,
ctx,
(&Some(lhs.dtype), a_ik.value),
Binop::normal(Operator::Mult),
(&Some(rhs.dtype), b_kj.value),
ctx.current_loc,
)?
.unwrap()
.to_basic_value_enum(ctx, generator, dst_dtype)?;
// dst_[...]ij += x
let dst_ij = ctx.builder.build_load(pdst_ij, "").unwrap();
let dst_ij = gen_binop_expr_with_values(
generator,
ctx,
(&Some(dst_dtype), dst_ij),
Binop::normal(Operator::Add),
(&Some(dst_dtype), x),
ctx.current_loc,
)?
.unwrap()
.to_basic_value_enum(ctx, generator, dst_dtype)?;
ctx.builder.build_store(pdst_ij, dst_ij).unwrap();
Ok(())
})
})
.unwrap();
dst
}
impl<'ctx> NDArrayObject<'ctx> {
/// Perform `np.matmul` according to the rules in
/// <https://numpy.org/doc/stable/reference/generated/numpy.matmul.html>.
///
/// This function always return an [`NDArrayObject`]. You may want to use [`NDArrayObject::split_unsized`]
/// to handle when the output could be a scalar.
///
/// `dst_dtype` defines the dtype of the returned ndarray.
pub fn matmul<G: CodeGenerator>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
a: Self,
b: Self,
out: NDArrayOut<'ctx>,
) -> Self {
// Sanity check, but type inference should prevent this.
assert!(a.ndims > 0 && b.ndims > 0, "np.matmul disallows scalar input");
/*
If both arguments are 2-D they are multiplied like conventional matrices.
If either argument is N-D, N > 2, it is treated as a stack of matrices residing in the last two indices and broadcast accordingly.
If the first argument is 1-D, it is promoted to a matrix by prepending a 1 to its dimensions. After matrix multiplication the prepended 1 is removed.
If the second argument is 1-D, it is promoted to a matrix by appending a 1 to its dimensions. After matrix multiplication the appended 1 is removed.
*/
let new_a = if a.ndims == 1 {
// Prepend 1 to its dimensions
a.index(generator, ctx, &[RustNDIndex::NewAxis, RustNDIndex::Ellipsis])
} else {
a
};
let new_b = if b.ndims == 1 {
// Append 1 to its dimensions
b.index(generator, ctx, &[RustNDIndex::Ellipsis, RustNDIndex::NewAxis])
} else {
b
};
// NOTE: `result` will always be a newly allocated ndarray.
// Current implementation cannot do in-place matrix muliplication.
let mut result = matmul_at_least_2d(generator, ctx, out.get_dtype(), new_a, new_b);
// Postprocessing on the result to remove prepended/appended axes.
let mut postindices = vec![];
let zero = Int(Int32).const_0(generator, ctx.ctx);
if a.ndims == 1 {
// Remove the prepended 1
postindices.push(RustNDIndex::SingleElement(zero));
}
if b.ndims == 1 {
// Remove the appended 1
postindices.push(RustNDIndex::Ellipsis);
postindices.push(RustNDIndex::SingleElement(zero));
}
if !postindices.is_empty() {
result = result.index(generator, ctx, &postindices);
}
match out {
NDArrayOut::NewNDArray { .. } => result,
NDArrayOut::WriteToNDArray { ndarray: out_ndarray } => {
let result_shape = result.instance.get(generator, ctx, |f| f.shape);
out_ndarray.assert_can_be_written_by_out(
generator,
ctx,
result.ndims,
result_shape,
);
out_ndarray.copy_data_from(generator, ctx, result);
out_ndarray
}
}
}
}

671
nac3core/src/codegen/object/ndarray/mod.rs
View File

@ -1,671 +0,0 @@
pub mod array;
pub mod broadcast;
pub mod contiguous;
pub mod factory;
pub mod indexing;
pub mod map;
pub mod matmul;
pub mod nditer;
pub mod shape_util;
pub mod view;
use inkwell::{
context::Context,
types::BasicType,
values::{BasicValue, BasicValueEnum, PointerValue},
AddressSpace,
};
use crate::{
codegen::{
irrt::{
call_nac3_ndarray_copy_data, call_nac3_ndarray_get_nth_pelement,
call_nac3_ndarray_get_pelement_by_indices, call_nac3_ndarray_is_c_contiguous,
call_nac3_ndarray_len, call_nac3_ndarray_nbytes,
call_nac3_ndarray_set_strides_by_shape, call_nac3_ndarray_size,
call_nac3_ndarray_util_assert_output_shape_same,
},
model::*,
CodeGenContext, CodeGenerator,
},
toplevel::{
helper::{create_ndims, extract_ndims},
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
},
typecheck::typedef::{Type, TypeEnum},
};
use super::{any::AnyObject, tuple::TupleObject};
/// Fields of [`NDArray`]
pub struct NDArrayFields<'ctx, F: FieldTraversal<'ctx>> {
pub data: F::Out<Ptr<Int<Byte>>>,
pub itemsize: F::Out<Int<SizeT>>,
pub ndims: F::Out<Int<SizeT>>,
pub shape: F::Out<Ptr<Int<SizeT>>>,
pub strides: F::Out<Ptr<Int<SizeT>>>,
}
/// A strided ndarray in NAC3.
///
/// See IRRT implementation for details about its fields.
#[derive(Debug, Clone, Copy, Default)]
pub struct NDArray;
impl<'ctx> StructKind<'ctx> for NDArray {
type Fields<F: FieldTraversal<'ctx>> = NDArrayFields<'ctx, F>;
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields {
data: traversal.add_auto("data"),
itemsize: traversal.add_auto("itemsize"),
ndims: traversal.add_auto("ndims"),
shape: traversal.add_auto("shape"),
strides: traversal.add_auto("strides"),
}
}
}
/// A NAC3 Python ndarray object.
#[derive(Debug, Clone, Copy)]
pub struct NDArrayObject<'ctx> {
pub dtype: Type,
pub ndims: u64,
pub instance: Instance<'ctx, Ptr<Struct<NDArray>>>,
}
impl<'ctx> NDArrayObject<'ctx> {
/// Attempt to convert an [`AnyObject`] into an [`NDArrayObject`].
pub fn from_object<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
object: AnyObject<'ctx>,
) -> NDArrayObject<'ctx> {
let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, object.ty);
let ndims = extract_ndims(&ctx.unifier, ndims);
Self::from_value_and_unpacked_types(generator, ctx, object.value, dtype, ndims)
}
/// Like [`NDArrayObject::from_object`] but you directly supply the ndarray's
/// `dtype` and `ndims`.
pub fn from_value_and_unpacked_types<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
value: V,
dtype: Type,
ndims: u64,
) -> Self {
let value = Ptr(Struct(NDArray)).check_value(generator, ctx.ctx, value).unwrap();
NDArrayObject { dtype, ndims, instance: value }
}
/// Get this ndarray's `ndims` as an LLVM constant.
pub fn ndims_llvm<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Instance<'ctx, Int<SizeT>> {
Int(SizeT).const_int(generator, ctx, self.ndims)
}
/// Get the typechecker ndarray type of this [`NDArrayObject`].
pub fn get_type(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Type {
let ndims = create_ndims(&mut ctx.unifier, self.ndims);
make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(self.dtype), Some(ndims))
}
/// Forget that this is an ndarray and convert into an [`AnyObject`].
pub fn to_any(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> AnyObject<'ctx> {
let ty = self.get_type(ctx);
AnyObject { value: self.instance.value.as_basic_value_enum(), ty }
}
/// Allocate an ndarray on the stack given its `ndims` and `dtype`.
///
/// `shape` and `strides` will be automatically allocated on the stack.
//e
/// The returned ndarray's content will be:
/// - `data`: set to `nullptr`.
/// - `itemsize`: set to the `sizeof()` of `dtype`.
/// - `ndims`: set to the value of `ndims`.
/// - `shape`: allocated with an array of length `ndims` with uninitialized values.
/// - `strides`: allocated with an array of length `ndims` with uninitialized values.
pub fn alloca<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
ndims: u64,
) -> Self {
let ndarray = Struct(NDArray).alloca(generator, ctx);
let data = Ptr(Int(Byte)).nullptr(generator, ctx.ctx);
ndarray.set(ctx, |f| f.data, data);
let itemsize = ctx.get_llvm_type(generator, dtype).size_of().unwrap();
let itemsize = Int(SizeT).z_extend_or_truncate(generator, ctx, itemsize);
ndarray.set(ctx, |f| f.itemsize, itemsize);
let ndims_val = Int(SizeT).const_int(generator, ctx.ctx, ndims);
ndarray.set(ctx, |f| f.ndims, ndims_val);
let shape = Int(SizeT).array_alloca(generator, ctx, ndims_val.value);
ndarray.set(ctx, |f| f.shape, shape);
let strides = Int(SizeT).array_alloca(generator, ctx, ndims_val.value);
ndarray.set(ctx, |f| f.strides, strides);
NDArrayObject { dtype, ndims, instance: ndarray }
}
/// Convenience function. Allocate an [`NDArrayObject`] with a statically known shape.
///
/// The returned [`NDArrayObject`]'s `data` and `strides` are uninitialized.
pub fn alloca_constant_shape<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
shape: &[u64],
) -> Self {
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, shape.len() as u64);
// Write shape
let dst_shape = ndarray.instance.get(generator, ctx, |f| f.shape);
for (i, dim) in shape.iter().enumerate() {
let dim = Int(SizeT).const_int(generator, ctx.ctx, *dim);
dst_shape.offset_const(ctx, i as u64).store(ctx, dim);
}
ndarray
}
/// Convenience function. Allocate an [`NDArrayObject`] with a dynamically known shape.
///
/// The returned [`NDArrayObject`]'s `data` and `strides` are uninitialized.
pub fn alloca_dynamic_shape<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
shape: &[Instance<'ctx, Int<SizeT>>],
) -> Self {
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, shape.len() as u64);
// Write shape
let dst_shape = ndarray.instance.get(generator, ctx, |f| f.shape);
for (i, dim) in shape.iter().enumerate() {
dst_shape.offset_const(ctx, i as u64).store(ctx, *dim);
}
ndarray
}
/// Initialize an ndarray's `data` by allocating a buffer on the stack.
/// The allocated data buffer is considered to be *owned* by the ndarray.
///
/// `strides` of the ndarray will also be updated with `set_strides_by_shape`.
///
/// `shape` and `itemsize` of the ndarray ***must*** be initialized first.
pub fn create_data<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) {
let nbytes = self.nbytes(generator, ctx);
let data = Int(Byte).array_alloca(generator, ctx, nbytes.value);
self.instance.set(ctx, |f| f.data, data);
self.set_strides_contiguous(generator, ctx);
}
/// Copy shape dimensions from an array.
pub fn copy_shape_from_array<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let num_items = self.ndims_llvm(generator, ctx.ctx).value;
self.instance.get(generator, ctx, |f| f.shape).copy_from(generator, ctx, shape, num_items);
}
/// Copy shape dimensions from an ndarray.
/// Panics if `ndims` mismatches.
pub fn copy_shape_from_ndarray<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_ndarray: NDArrayObject<'ctx>,
) {
assert_eq!(self.ndims, src_ndarray.ndims);
let src_shape = src_ndarray.instance.get(generator, ctx, |f| f.shape);
self.copy_shape_from_array(generator, ctx, src_shape);
}
/// Copy strides dimensions from an array.
pub fn copy_strides_from_array<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
strides: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let num_items = self.ndims_llvm(generator, ctx.ctx).value;
self.instance
.get(generator, ctx, |f| f.strides)
.copy_from(generator, ctx, strides, num_items);
}
/// Copy strides dimensions from an ndarray.
/// Panics if `ndims` mismatches.
pub fn copy_strides_from_ndarray<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_ndarray: NDArrayObject<'ctx>,
) {
assert_eq!(self.ndims, src_ndarray.ndims);
let src_strides = src_ndarray.instance.get(generator, ctx, |f| f.strides);
self.copy_strides_from_array(generator, ctx, src_strides);
}
/// Get the `np.size()` of this ndarray.
pub fn size<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<SizeT>> {
call_nac3_ndarray_size(generator, ctx, self.instance)
}
/// Get the `ndarray.nbytes` of this ndarray.
pub fn nbytes<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<SizeT>> {
call_nac3_ndarray_nbytes(generator, ctx, self.instance)
}
/// Get the `len()` of this ndarray.
pub fn len<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<SizeT>> {
call_nac3_ndarray_len(generator, ctx, self.instance)
}
/// Check if this ndarray is C-contiguous.
///
/// See NumPy's `flags["C_CONTIGUOUS"]`: <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags>
pub fn is_c_contiguous<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<Bool>> {
call_nac3_ndarray_is_c_contiguous(generator, ctx, self.instance)
}
/// Get the pointer to the n-th (0-based) element.
///
/// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
pub fn get_nth_pelement<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
nth: Instance<'ctx, Int<SizeT>>,
) -> PointerValue<'ctx> {
let elem_ty = ctx.get_llvm_type(generator, self.dtype);
let p = call_nac3_ndarray_get_nth_pelement(generator, ctx, self.instance, nth);
ctx.builder
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), "")
.unwrap()
}
/// Get the n-th (0-based) scalar.
pub fn get_nth_scalar<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
nth: Instance<'ctx, Int<SizeT>>,
) -> AnyObject<'ctx> {
let ptr = self.get_nth_pelement(generator, ctx, nth);
let value = ctx.builder.build_load(ptr, "").unwrap();
AnyObject { ty: self.dtype, value }
}
/// Get the pointer to the element indexed by `indices`.
///
/// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
pub fn get_pelement_by_indices<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
indices: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> PointerValue<'ctx> {
let elem_ty = ctx.get_llvm_type(generator, self.dtype);
let p = call_nac3_ndarray_get_pelement_by_indices(generator, ctx, self.instance, indices);
ctx.builder
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), "")
.unwrap()
}
/// Get the scalar indexed by `indices`.
pub fn get_scalar_by_indices<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
indices: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> AnyObject<'ctx> {
let ptr = self.get_pelement_by_indices(generator, ctx, indices);
let value = ctx.builder.build_load(ptr, "").unwrap();
AnyObject { ty: self.dtype, value }
}
/// Call [`call_nac3_ndarray_set_strides_by_shape`] on this ndarray to update `strides`.
///
/// Update the ndarray's strides to make the ndarray contiguous.
pub fn set_strides_contiguous<G: CodeGenerator + ?Sized>(
self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) {
call_nac3_ndarray_set_strides_by_shape(generator, ctx, self.instance);
}
/// Clone/Copy this ndarray - Allocate a new ndarray with the same shape as this ndarray and copy the contents over.
///
/// The new ndarray will own its data and will be C-contiguous.
#[must_use]
pub fn make_copy<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Self {
let clone = NDArrayObject::alloca(generator, ctx, self.dtype, self.ndims);
let shape = self.instance.gep(ctx, |f| f.shape).load(generator, ctx);
clone.copy_shape_from_array(generator, ctx, shape);
clone.create_data(generator, ctx);
clone.copy_data_from(generator, ctx, *self);
clone
}
/// Copy data from another ndarray.
///
/// This ndarray and `src` is that their `np.size()` should be the same. Their shapes
/// do not matter. The copying order is determined by how their flattened views look.
///
/// Panics if the `dtype`s of ndarrays are different.
pub fn copy_data_from<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src: NDArrayObject<'ctx>,
) {
assert!(ctx.unifier.unioned(self.dtype, src.dtype), "self and src dtype should match");
call_nac3_ndarray_copy_data(generator, ctx, src.instance, self.instance);
}
/// Returns true if this ndarray is unsized - `ndims == 0` and only contains a scalar.
#[must_use]
pub fn is_unsized(&self) -> bool {
self.ndims == 0
}
/// If this ndarray is unsized, return its sole value as an [`AnyObject`].
/// Otherwise, do nothing and return the ndarray itself.
pub fn split_unsized<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> ScalarOrNDArray<'ctx> {
if self.is_unsized() {
// NOTE: `np.size(self) == 0` here is never possible.
let zero = Int(SizeT).const_0(generator, ctx.ctx);
let value = self.get_nth_scalar(generator, ctx, zero).value;
ScalarOrNDArray::Scalar(AnyObject { ty: self.dtype, value })
} else {
ScalarOrNDArray::NDArray(*self)
}
}
/// Fill the ndarray with a scalar.
///
/// `fill_value` must have the same LLVM type as the `dtype` of this ndarray.
pub fn fill<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
value: BasicValueEnum<'ctx>,
) {
self.foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
let p = nditer.get_pointer(generator, ctx);
ctx.builder.build_store(p, value).unwrap();
Ok(())
})
.unwrap();
}
/// Create the shape tuple of this ndarray like `np.shape(<ndarray>)`.
///
/// The returned integers in the tuple are in int32.
pub fn make_shape_tuple<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> TupleObject<'ctx> {
// TODO: Return a tuple of SizeT
let mut objects = Vec::with_capacity(self.ndims as usize);
for i in 0..self.ndims {
let dim = self
.instance
.get(generator, ctx, |f| f.shape)
.get_index_const(generator, ctx, i)
.truncate_or_bit_cast(generator, ctx, Int32);
objects.push(AnyObject {
ty: ctx.primitives.int32,
value: dim.value.as_basic_value_enum(),
});
}
TupleObject::from_objects(generator, ctx, objects)
}
/// Create the strides tuple of this ndarray like `np.strides(<ndarray>)`.
///
/// The returned integers in the tuple are in int32.
pub fn make_strides_tuple<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> TupleObject<'ctx> {
// TODO: Return a tuple of SizeT.
let mut objects = Vec::with_capacity(self.ndims as usize);
for i in 0..self.ndims {
let dim = self
.instance
.get(generator, ctx, |f| f.strides)
.get_index_const(generator, ctx, i)
.truncate_or_bit_cast(generator, ctx, Int32);
objects.push(AnyObject {
ty: ctx.primitives.int32,
value: dim.value.as_basic_value_enum(),
});
}
TupleObject::from_objects(generator, ctx, objects)
}
/// Create an unsized ndarray to contain `object`.
pub fn make_unsized<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
object: AnyObject<'ctx>,
) -> NDArrayObject<'ctx> {
// We have to put the value on the stack to get a data pointer.
let data = ctx.builder.build_alloca(object.value.get_type(), "make_unsized").unwrap();
ctx.builder.build_store(data, object.value).unwrap();
let data = Ptr(Int(Byte)).pointer_cast(generator, ctx, data);
let ndarray = NDArrayObject::alloca(generator, ctx, object.ty, 0);
ndarray.instance.set(ctx, |f| f.data, data);
ndarray
}
/// Check if this `NDArray` can be used as an `out` ndarray for an operation.
///
/// Raise an exception if the shapes do not match.
pub fn assert_can_be_written_by_out<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
out_ndims: u64,
out_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let ndarray_ndims = self.ndims_llvm(generator, ctx.ctx);
let ndarray_shape = self.instance.get(generator, ctx, |f| f.shape);
let output_ndims = Int(SizeT).const_int(generator, ctx.ctx, out_ndims);
let output_shape = out_shape;
call_nac3_ndarray_util_assert_output_shape_same(
generator,
ctx,
ndarray_ndims,
ndarray_shape,
output_ndims,
output_shape,
);
}
}
/// A convenience enum for implementing functions that acts on scalars or ndarrays or both.
#[derive(Debug, Clone, Copy)]
pub enum ScalarOrNDArray<'ctx> {
Scalar(AnyObject<'ctx>),
NDArray(NDArrayObject<'ctx>),
}
impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for AnyObject<'ctx> {
type Error = ();
fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
match value {
ScalarOrNDArray::Scalar(scalar) => Ok(*scalar),
ScalarOrNDArray::NDArray(_ndarray) => Err(()),
}
}
}
impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for NDArrayObject<'ctx> {
type Error = ();
fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
match value {
ScalarOrNDArray::Scalar(_scalar) => Err(()),
ScalarOrNDArray::NDArray(ndarray) => Ok(*ndarray),
}
}
}
impl<'ctx> ScalarOrNDArray<'ctx> {
/// Split on `object` either into a scalar or an ndarray.
///
/// If `object` is an ndarray, [`ScalarOrNDArray::NDArray`].
///
/// For everything else, it is wrapped with [`ScalarOrNDArray::Scalar`].
pub fn split_object<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
object: AnyObject<'ctx>,
) -> ScalarOrNDArray<'ctx> {
match &*ctx.unifier.get_ty(object.ty) {
TypeEnum::TObj { obj_id, .. }
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
{
let ndarray = NDArrayObject::from_object(generator, ctx, object);
ScalarOrNDArray::NDArray(ndarray)
}
_ => ScalarOrNDArray::Scalar(object),
}
}
/// Get the underlying [`BasicValueEnum<'ctx>`] of this [`ScalarOrNDArray`].
#[must_use]
pub fn to_basic_value_enum(self) -> BasicValueEnum<'ctx> {
match self {
ScalarOrNDArray::Scalar(scalar) => scalar.value,
ScalarOrNDArray::NDArray(ndarray) => ndarray.instance.value.as_basic_value_enum(),
}
}
/// Convert this [`ScalarOrNDArray`] to an ndarray - behaves like `np.asarray`.
/// - If this is an ndarray, the ndarray is returned.
/// - If this is a scalar, this function returns new ndarray created with [`NDArrayObject::make_unsized`].
pub fn to_ndarray<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> NDArrayObject<'ctx> {
match self {
ScalarOrNDArray::NDArray(ndarray) => *ndarray,
ScalarOrNDArray::Scalar(scalar) => NDArrayObject::make_unsized(generator, ctx, *scalar),
}
}
/// Get the dtype of the ndarray created if this were called with [`ScalarOrNDArray::to_ndarray`].
#[must_use]
pub fn get_dtype(&self) -> Type {
match self {
ScalarOrNDArray::NDArray(ndarray) => ndarray.dtype,
ScalarOrNDArray::Scalar(scalar) => scalar.ty,
}
}
}
/// An helper enum specifying how a function should produce its output.
///
/// Many functions in NumPy has an optional `out` parameter (e.g., `matmul`). If `out` is specified
/// with an ndarray, the result of a function will be written to `out`. If `out` is not specified, a function will
/// create a new ndarray and store the result in it.
#[derive(Debug, Clone, Copy)]
pub enum NDArrayOut<'ctx> {
/// Tell a function should create a new ndarray with the expected element type `dtype`.
NewNDArray { dtype: Type },
/// Tell a function to write the result to `ndarray`.
WriteToNDArray { ndarray: NDArrayObject<'ctx> },
}
impl<'ctx> NDArrayOut<'ctx> {
/// Get the dtype of this output.
#[must_use]
pub fn get_dtype(&self) -> Type {
match self {
NDArrayOut::NewNDArray { dtype } => *dtype,
NDArrayOut::WriteToNDArray { ndarray } => ndarray.dtype,
}
}
}
/// A version of [`call_nac3_ndarray_set_strides_by_shape`] in Rust.
///
/// This function is used generating strides for globally defined contiguous ndarrays.
#[must_use]
pub fn make_contiguous_strides(itemsize: u64, ndims: u64, shape: &[u64]) -> Vec<u64> {
let mut strides = Vec::with_capacity(ndims as usize);
let mut stride_product = 1u64;
for i in 0..ndims {
let axis = ndims - i - 1;
strides[axis as usize] = stride_product * itemsize;
stride_product *= shape[axis as usize];
}
strides
}

168
nac3core/src/codegen/object/ndarray/nditer.rs
View File

@ -1,168 +0,0 @@
use inkwell::{types::BasicType, values::PointerValue, AddressSpace};
use crate::codegen::{
irrt::{call_nac3_nditer_has_next, call_nac3_nditer_initialize, call_nac3_nditer_next},
model::*,
object::any::AnyObject,
stmt::{gen_for_callback, BreakContinueHooks},
CodeGenContext, CodeGenerator,
};
use super::NDArrayObject;
/// Fields of [`NDIter`]
pub struct NDIterFields<'ctx, F: FieldTraversal<'ctx>> {
pub ndims: F::Out<Int<SizeT>>,
pub shape: F::Out<Ptr<Int<SizeT>>>,
pub strides: F::Out<Ptr<Int<SizeT>>>,
pub indices: F::Out<Ptr<Int<SizeT>>>,
pub nth: F::Out<Int<SizeT>>,
pub element: F::Out<Ptr<Int<Byte>>>,
pub size: F::Out<Int<SizeT>>,
}
/// An IRRT helper structure used to iterate through an ndarray.
#[derive(Debug, Clone, Copy, Default)]
pub struct NDIter;
impl<'ctx> StructKind<'ctx> for NDIter {
type Fields<F: FieldTraversal<'ctx>> = NDIterFields<'ctx, F>;
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields {
ndims: traversal.add_auto("ndims"),
shape: traversal.add_auto("shape"),
strides: traversal.add_auto("strides"),
indices: traversal.add_auto("indices"),
nth: traversal.add_auto("nth"),
element: traversal.add_auto("element"),
size: traversal.add_auto("size"),
}
}
}
/// A helper structure containing extra details of an [`NDIter`].
#[derive(Debug, Clone)]
pub struct NDIterHandle<'ctx> {
instance: Instance<'ctx, Ptr<Struct<NDIter>>>,
/// The ndarray this [`NDIter`] to iterating over.
ndarray: NDArrayObject<'ctx>,
/// The current indices of [`NDIter`].
indices: Instance<'ctx, Ptr<Int<SizeT>>>,
}
impl<'ctx> NDIterHandle<'ctx> {
/// Allocate an [`NDIter`] that iterates through an ndarray.
pub fn new<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: NDArrayObject<'ctx>,
) -> Self {
let nditer = Struct(NDIter).alloca(generator, ctx);
let ndims = ndarray.ndims_llvm(generator, ctx.ctx);
// The caller has the responsibility to allocate 'indices' for `NDIter`.
let indices = Int(SizeT).array_alloca(generator, ctx, ndims.value);
call_nac3_nditer_initialize(generator, ctx, nditer, ndarray.instance, indices);
NDIterHandle { ndarray, instance: nditer, indices }
}
#[must_use]
pub fn has_next<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<Bool>> {
call_nac3_nditer_has_next(generator, ctx, self.instance)
}
pub fn next<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) {
call_nac3_nditer_next(generator, ctx, self.instance);
}
/// Get pointer to the current element.
#[must_use]
pub fn get_pointer<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> PointerValue<'ctx> {
let elem_ty = ctx.get_llvm_type(generator, self.ndarray.dtype);
let p = self.instance.get(generator, ctx, |f| f.element);
ctx.builder
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), "element")
.unwrap()
}
/// Get the value of the current element.
#[must_use]
pub fn get_scalar<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> AnyObject<'ctx> {
let p = self.get_pointer(generator, ctx);
let value = ctx.builder.build_load(p, "value").unwrap();
AnyObject { ty: self.ndarray.dtype, value }
}
/// Get the index of the current element.
#[must_use]
pub fn get_index<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<SizeT>> {
self.instance.get(generator, ctx, |f| f.nth)
}
/// Get the indices of the current element.
#[must_use]
pub fn get_indices(&self) -> Instance<'ctx, Ptr<Int<SizeT>>> {
self.indices
}
}
impl<'ctx> NDArrayObject<'ctx> {
/// Iterate through every element in the ndarray.
///
/// `body` also access to [`BreakContinueHooks`] to short-circuit.
pub fn foreach<'a, G, F>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, 'a>,
body: F,
) -> Result<(), String>
where
G: CodeGenerator + ?Sized,
F: FnOnce(
&mut G,
&mut CodeGenContext<'ctx, 'a>,
BreakContinueHooks<'ctx>,
NDIterHandle<'ctx>,
) -> Result<(), String>,
{
gen_for_callback(
generator,
ctx,
Some("ndarray_foreach"),
|generator, ctx| Ok(NDIterHandle::new(generator, ctx, *self)),
|generator, ctx, nditer| Ok(nditer.has_next(generator, ctx).value),
|generator, ctx, hooks, nditer| body(generator, ctx, hooks, nditer),
|generator, ctx, nditer| {
nditer.next(generator, ctx);
Ok(())
},
)
}
}

105
nac3core/src/codegen/object/ndarray/shape_util.rs
View File

@ -1,105 +0,0 @@
use util::gen_for_model;
use crate::{
codegen::{
model::*,
object::{any::AnyObject, list::ListObject, tuple::TupleObject},
CodeGenContext, CodeGenerator,
},
typecheck::typedef::TypeEnum,
};
/// Parse a NumPy-like "int sequence" input and return the int sequence as an array and its length.
///
/// * `sequence` - The `sequence` parameter.
/// * `sequence_ty` - The typechecker type of `sequence`
///
/// The `sequence` argument type may only be one of the following:
/// 1. A list of `int32`; e.g., `np.empty([600, 800, 3])`
/// 2. A tuple of `int32`; e.g., `np.empty((600, 800, 3))`
/// 3. A scalar `int32`; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
///
/// All `int32` values will be sign-extended to `SizeT`.
pub fn parse_numpy_int_sequence<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
input_sequence: AnyObject<'ctx>,
) -> (Instance<'ctx, Int<SizeT>>, Instance<'ctx, Ptr<Int<SizeT>>>) {
let zero = Int(SizeT).const_0(generator, ctx.ctx);
let one = Int(SizeT).const_1(generator, ctx.ctx);
// The result `list` to return.
match &*ctx.unifier.get_ty(input_sequence.ty) {
TypeEnum::TObj { obj_id, .. }
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
{
// 1. A list of `int32`; e.g., `np.empty([600, 800, 3])`
// Check `input_sequence`
let input_sequence = ListObject::from_object(generator, ctx, input_sequence);
let len = input_sequence.instance.get(generator, ctx, |f| f.len);
let result = Int(SizeT).array_alloca(generator, ctx, len.value);
// Load all the `int32`s from the input_sequence, cast them to `SizeT`, and store them into `result`
gen_for_model(generator, ctx, zero, len, one, |generator, ctx, _hooks, i| {
// Load the i-th int32 in the input sequence
let int = input_sequence
.instance
.get(generator, ctx, |f| f.items)
.get_index(generator, ctx, i.value)
.value
.into_int_value();
// Cast to SizeT
let int = Int(SizeT).s_extend_or_bit_cast(generator, ctx, int);
// Store
result.set_index(ctx, i.value, int);
Ok(())
})
.unwrap();
(len, result)
}
TypeEnum::TTuple { .. } => {
// 2. A tuple of ints; e.g., `np.empty((600, 800, 3))`
let input_sequence = TupleObject::from_object(ctx, input_sequence);
let len = input_sequence.len(generator, ctx);
let result = Int(SizeT).array_alloca(generator, ctx, len.value);
for i in 0..input_sequence.num_elements() {
// Get the i-th element off of the tuple and load it into `result`.
let int = input_sequence.index(ctx, i).value.into_int_value();
let int = Int(SizeT).s_extend_or_bit_cast(generator, ctx, int);
result.set_index_const(ctx, i as u64, int);
}
(len, result)
}
TypeEnum::TObj { obj_id, .. }
if *obj_id == ctx.primitives.int32.obj_id(&ctx.unifier).unwrap() =>
{
// 3. A scalar int; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
let input_int = input_sequence.value.into_int_value();
let len = Int(SizeT).const_1(generator, ctx.ctx);
let result = Int(SizeT).array_alloca(generator, ctx, len.value);
let int = Int(SizeT).s_extend_or_bit_cast(generator, ctx, input_int);
// Storing into result[0]
result.store(ctx, int);
(len, result)
}
_ => panic!(
"encountered unknown sequence type: {}",
ctx.unifier.stringify(input_sequence.ty)
),
}
}

3
nac3core/src/codegen/object/ndarray/str.rs
View File

@ -1,3 +0,0 @@
pub fn str_type() {
}

119
nac3core/src/codegen/object/ndarray/view.rs
View File

@ -1,119 +0,0 @@
use crate::codegen::{
irrt::{call_nac3_ndarray_reshape_resolve_and_check_new_shape, call_nac3_ndarray_transpose},
model::*,
CodeGenContext, CodeGenerator,
};
use super::{indexing::RustNDIndex, NDArrayObject};
impl<'ctx> NDArrayObject<'ctx> {
/// Make sure the ndarray is at least `ndmin`-dimensional.
///
/// If this ndarray's `ndims` is less than `ndmin`, a view is created on this with 1s prepended to the shape.
/// If this ndarray's `ndims` is not less than `ndmin`, this function does nothing and return this ndarray.
#[must_use]
pub fn atleast_nd<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndmin: u64,
) -> Self {
if self.ndims < ndmin {
// return this_ndarray[np.newaxis, np.newaxis, and more, ...]
let mut indices = vec![];
for _ in self.ndims..ndmin {
indices.push(RustNDIndex::NewAxis);
}
indices.push(RustNDIndex::Ellipsis);
self.index(generator, ctx, &indices)
} else {
*self
}
}
/// Create a reshaped view on this ndarray like `np.reshape()`.
///
/// If there is a `-1` in `new_shape`, it will be resolved; `new_shape` would **NOT** be modified as a result.
///
/// If reshape without copying is impossible, this function will allocate a new ndarray and copy contents.
///
/// * `new_ndims` - The number of dimensions of `new_shape` as a [`Type`].
/// * `new_shape` - The target shape to do `np.reshape()`.
#[must_use]
pub fn reshape_or_copy<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
new_ndims: u64,
new_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> Self {
// TODO: The current criterion for whether to do a full copy or not is by checking `is_c_contiguous`,
// but this is not optimal - there are cases when the ndarray is not contiguous but could be reshaped
// without copying data. Look into how numpy does it.
let current_bb = ctx.builder.get_insert_block().unwrap();
let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then_bb");
let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "else_bb");
let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "end_bb");
let dst_ndarray = NDArrayObject::alloca(generator, ctx, self.dtype, new_ndims);
dst_ndarray.copy_shape_from_array(generator, ctx, new_shape);
// Reolsve negative indices
let size = self.size(generator, ctx);
let dst_ndims = dst_ndarray.ndims_llvm(generator, ctx.ctx);
let dst_shape = dst_ndarray.instance.get(generator, ctx, |f| f.shape);
call_nac3_ndarray_reshape_resolve_and_check_new_shape(
generator, ctx, size, dst_ndims, dst_shape,
);
let is_c_contiguous = self.is_c_contiguous(generator, ctx);
ctx.builder.build_conditional_branch(is_c_contiguous.value, then_bb, else_bb).unwrap();
// Inserting into then_bb: reshape is possible without copying
ctx.builder.position_at_end(then_bb);
dst_ndarray.set_strides_contiguous(generator, ctx);
dst_ndarray.instance.set(ctx, |f| f.data, self.instance.get(generator, ctx, |f| f.data));
ctx.builder.build_unconditional_branch(end_bb).unwrap();
// Inserting into else_bb: reshape is impossible without copying
ctx.builder.position_at_end(else_bb);
dst_ndarray.create_data(generator, ctx);
dst_ndarray.copy_data_from(generator, ctx, *self);
ctx.builder.build_unconditional_branch(end_bb).unwrap();
// Reposition for continuation
ctx.builder.position_at_end(end_bb);
dst_ndarray
}
/// Create a transposed view on this ndarray like `np.transpose(<ndarray>, <axes> = None)`.
/// * `axes` - If specified, should be an array of the permutation (negative indices are **allowed**).
#[must_use]
pub fn transpose<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
axes: Option<Instance<'ctx, Ptr<Int<SizeT>>>>,
) -> Self {
// Define models
let transposed_ndarray = NDArrayObject::alloca(generator, ctx, self.dtype, self.ndims);
let num_axes = self.ndims_llvm(generator, ctx.ctx);
// `axes = nullptr` if `axes` is unspecified.
let axes = axes.unwrap_or_else(|| Ptr(Int(SizeT)).nullptr(generator, ctx.ctx));
call_nac3_ndarray_transpose(
generator,
ctx,
self.instance,
transposed_ndarray.instance,
num_axes,
axes,
);
transposed_ndarray
}
}

139
nac3core/src/codegen/object/range.rs
View File

@ -1,139 +0,0 @@
use inkwell::{values::IntValue, IntPredicate};
use crate::codegen::{irrt::call_nac3_range_len, model::*, CodeGenContext, CodeGenerator};
use super::any::AnyObject;
/// A range in NAC3.
pub type Range<N> = Array<Len<3>, Int<N>>;
/// An alias for `Range::<Int32>::default()`
#[must_use]
pub fn range_model() -> Range<Int32> {
Array::default()
}
impl<'ctx, N: IntKind<'ctx>> Instance<'ctx, Ptr<Range<N>>> {
/// Get GEP to `range.start`.
pub fn start(&self, ctx: &CodeGenContext<'ctx, '_>) -> Instance<'ctx, Ptr<Int<N>>> {
self.gep_const(ctx, 0)
}
/// Get GEP to `range.stop`.
pub fn stop(&self, ctx: &CodeGenContext<'ctx, '_>) -> Instance<'ctx, Ptr<Int<N>>> {
self.gep_const(ctx, 1)
}
/// Get GEP to `range.step`.
pub fn step(&self, ctx: &CodeGenContext<'ctx, '_>) -> Instance<'ctx, Ptr<Int<N>>> {
self.gep_const(ctx, 2)
}
/// Convenience function to load the `(start, stop, step)` of this range.
#[allow(clippy::type_complexity)]
pub fn destructure<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
) -> (Instance<'ctx, Int<N>>, Instance<'ctx, Int<N>>, Instance<'ctx, Int<N>>) {
let start = self.start(ctx).load(generator, ctx);
let stop = self.stop(ctx).load(generator, ctx);
let step = self.step(ctx).load(generator, ctx);
(start, stop, step)
}
}
/// Generate LLVM IR to check that a range's `step` is not zero.
/// Throws "range step must not be zero" if it is the case.
pub fn assert_range_step_non_zero<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
step: IntValue<'ctx>,
) {
let int32 = ctx.ctx.i32_type();
let rangenez =
ctx.builder.build_int_compare(IntPredicate::NE, step, int32.const_zero(), "").unwrap();
ctx.make_assert(
generator,
rangenez,
"0:ValueError",
"range step must not be zero",
[None, None, None],
ctx.current_loc,
);
}
/// A Rust structure that has [`Range`] utilities and looks like a [`Range`] but
/// `start`, `stop` and `step` are held by LLVM registers only.
///
/// This structure exists because many implementations use [`Range`] utilities but
/// it might not be good to alloca an actual [`Range`] value on the stack in order
/// to perform calculations.
pub struct RustRange<'ctx, N: IntKind<'ctx>> {
pub start: Instance<'ctx, Int<N>>,
pub stop: Instance<'ctx, Int<N>>,
pub step: Instance<'ctx, Int<N>>,
}
impl<'ctx, N: IntKind<'ctx>> RustRange<'ctx, N> {
pub fn assert_step_non_zero<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) {
assert_range_step_non_zero(generator, ctx, self.step.value);
}
/// Calculate the `len()` of this range.
pub fn len<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<N>> {
let int_kind = self.start.model.0;
call_nac3_range_len(generator, ctx, int_kind, self.start, self.stop, self.step)
}
}
// TODO: `RangeObject` in the future will have range32, range64
/// A NAC3 Python range object.
#[derive(Debug, Clone, Copy)]
pub struct RangeObject<'ctx> {
pub instance: Instance<'ctx, Ptr<Range<Int32>>>,
}
impl<'ctx> RangeObject<'ctx> {
/// Attempt to convert an [`AnyObject`] into a [`RangeObject`].
pub fn from_object<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
object: AnyObject<'ctx>,
) -> RangeObject<'ctx> {
assert!(ctx.unifier.unioned(object.ty, ctx.primitives.range));
let instance = Ptr(Range::default()).check_value(generator, ctx.ctx, object.value).unwrap();
RangeObject { instance }
}
/// Convert into a [`RustRange`].
pub fn as_rust_range<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> RustRange<'ctx, Int32> {
let (start, stop, step) = self.instance.destructure(generator, ctx);
RustRange { start, stop, step }
}
/// Get the `len()` of this range.
pub fn len<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<Int32>> {
let range = self.as_rust_range(generator, ctx);
range.assert_step_non_zero(generator, ctx);
range.len(generator, ctx)
}
}

185
nac3core/src/codegen/object/slice.rs
View File

@ -1,185 +0,0 @@
use crate::codegen::{irrt::call_nac3_slice_indices, model::*, CodeGenContext, CodeGenerator};
use super::range::RustRange;
/// Fields of [`Slice`]
#[derive(Debug, Clone)]
pub struct SliceFields<'ctx, F: FieldTraversal<'ctx>, N: IntKind<'ctx>> {
pub start_defined: F::Out<Int<Bool>>,
pub start: F::Out<Int<N>>,
pub stop_defined: F::Out<Int<Bool>>,
pub stop: F::Out<Int<N>>,
pub step_defined: F::Out<Int<Bool>>,
pub step: F::Out<Int<N>>,
}
/// An IRRT representation of an (unresolved) slice.
#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
pub struct Slice<N>(pub N);
impl<'ctx, N: IntKind<'ctx>> StructKind<'ctx> for Slice<N> {
type Fields<F: FieldTraversal<'ctx>> = SliceFields<'ctx, F, N>;
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields {
start_defined: traversal.add_auto("start_defined"),
start: traversal.add("start", Int(self.0)),
stop_defined: traversal.add_auto("stop_defined"),
stop: traversal.add("stop", Int(self.0)),
step_defined: traversal.add_auto("step_defined"),
step: traversal.add("step", Int(self.0)),
}
}
}
/// A Rust structure that has [`Slice`] utilities and looks like a [`Slice`] but
/// `start`, `stop` and `step` are held by LLVM registers only and possibly
/// [`Option::None`] if unspecified.
#[derive(Debug, Clone)]
pub struct RustSlice<'ctx, N: IntKind<'ctx>> {
// It is possible that `start`, `stop`, and `step` are all `None`.
// We need to know the `int_kind` even when that is the case.
pub int_kind: N,
pub start: Option<Instance<'ctx, Int<N>>>,
pub stop: Option<Instance<'ctx, Int<N>>>,
pub step: Option<Instance<'ctx, Int<N>>>,
}
impl<'ctx, N: IntKind<'ctx>> RustSlice<'ctx, N> {
/// Write the contents to an LLVM [`Slice`].
pub fn write_to_slice<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
dst_slice_ptr: Instance<'ctx, Ptr<Struct<Slice<N>>>>,
) {
let false_ = Int(Bool).const_false(generator, ctx.ctx);
let true_ = Int(Bool).const_true(generator, ctx.ctx);
match self.start {
Some(start) => {
dst_slice_ptr.gep(ctx, |f| f.start_defined).store(ctx, true_);
dst_slice_ptr.gep(ctx, |f| f.start).store(ctx, start);
}
None => dst_slice_ptr.gep(ctx, |f| f.start_defined).store(ctx, false_),
}
match self.stop {
Some(stop) => {
dst_slice_ptr.gep(ctx, |f| f.stop_defined).store(ctx, true_);
dst_slice_ptr.gep(ctx, |f| f.stop).store(ctx, stop);
}
None => dst_slice_ptr.gep(ctx, |f| f.stop_defined).store(ctx, false_),
}
match self.step {
Some(step) => {
dst_slice_ptr.gep(ctx, |f| f.step_defined).store(ctx, true_);
dst_slice_ptr.gep(ctx, |f| f.step).store(ctx, step);
}
None => dst_slice_ptr.gep(ctx, |f| f.step_defined).store(ctx, false_),
}
}
/// Resolve this [`RustSlice`] into a [`RustRange`] like `slice.indices` in Python.
///
/// NOTE: This function does stack allocation.
pub fn indices<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
length: Instance<'ctx, Int<N>>,
) -> RustRange<'ctx, N> {
let mut is_defined = |value: Option<_>| -> Instance<'ctx, Int<Bool>> {
Int(Bool).const_int(generator, ctx.ctx, u64::from(value.is_some()))
};
let start_defined = is_defined(self.start);
let stop_defined = is_defined(self.stop);
let step_defined = is_defined(self.step);
let mut defined_or_zero = |value: Option<_>| -> Instance<'ctx, Int<N>> {
if let Some(value) = value {
value
} else {
// If undefined, return 0 as a placeholder.
Int(self.int_kind).const_0(generator, ctx.ctx)
}
};
let start = defined_or_zero(self.start);
let stop = defined_or_zero(self.stop);
let step = defined_or_zero(self.step);
// Stack allocation here.
let range_start = Int(self.int_kind).alloca(generator, ctx);
let range_stop = Int(self.int_kind).alloca(generator, ctx);
let range_step = Int(self.int_kind).alloca(generator, ctx);
call_nac3_slice_indices(
generator,
ctx,
self.int_kind,
start_defined,
start,
stop_defined,
stop,
step_defined,
step,
length,
range_start,
range_stop,
range_step,
);
let start = range_start.load(generator, ctx);
let stop = range_stop.load(generator, ctx);
let step = range_step.load(generator, ctx);
RustRange { start, stop, step }
}
}
pub mod util {
use nac3parser::ast::Expr;
use crate::{
codegen::{model::*, CodeGenContext, CodeGenerator},
typecheck::typedef::Type,
};
use super::RustSlice;
/// Generate LLVM IR for an [`ExprKind::Slice`] and convert it into a [`RustSlice`].
#[allow(clippy::type_complexity)]
pub fn gen_slice<'ctx, G: CodeGenerator>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
lower: &Option<Box<Expr<Option<Type>>>>,
upper: &Option<Box<Expr<Option<Type>>>>,
step: &Option<Box<Expr<Option<Type>>>>,
) -> Result<RustSlice<'ctx, Int32>, String> {
let mut help = |value_expr: &Option<Box<Expr<Option<Type>>>>| -> Result<_, String> {
Ok(match value_expr {
None => None,
Some(value_expr) => {
let value_expr = generator
.gen_expr(ctx, value_expr)?
.unwrap()
.to_basic_value_enum(ctx, generator, ctx.primitives.int32)?;
let value_expr =
Int(Int32).check_value(generator, ctx.ctx, value_expr).unwrap();
Some(value_expr)
}
})
};
let start = help(lower)?;
let stop = help(upper)?;
let step = help(step)?;
Ok(RustSlice { int_kind: Int32, start, stop, step })
}
}

11
nac3core/src/codegen/object/str.rs
View File

@ -1,11 +0,0 @@
use super::cslice::CSlice;
use crate::codegen::model::*;
/// A string in NAC3.
pub type Str = Struct<CSlice<Int<Byte>>>;
/// An alias for `Str::default()`
#[must_use]
pub fn str_model() -> Str {
Str::default()
}

99
nac3core/src/codegen/object/tuple.rs
View File

@ -1,99 +0,0 @@
use inkwell::values::StructValue;
use itertools::Itertools;
use crate::{
codegen::{model::*, CodeGenContext, CodeGenerator},
typecheck::typedef::{Type, TypeEnum},
};
use super::any::AnyObject;
/// A NAC3 tuple object.
///
/// NOTE: This struct has no copy trait.
#[derive(Debug, Clone)]
pub struct TupleObject<'ctx> {
/// The type of the tuple.
pub tys: Vec<Type>,
/// The underlying LLVM struct value of this tuple.
pub value: StructValue<'ctx>,
}
impl<'ctx> TupleObject<'ctx> {
pub fn from_object(ctx: &mut CodeGenContext<'ctx, '_>, object: AnyObject<'ctx>) -> Self {
// TODO: Keep `is_vararg_ctx` from TTuple?
// Sanity check on object type.
let TypeEnum::TTuple { ty: tys, .. } = &*ctx.unifier.get_ty(object.ty) else {
panic!(
"Expected type to be a TypeEnum::TTuple, got {}",
ctx.unifier.stringify(object.ty)
);
};
// Check number of fields
let value = object.value.into_struct_value();
let value_num_fields = value.get_type().count_fields() as usize;
assert!(
value_num_fields == tys.len(),
"Tuple type has {} item(s), but the LLVM struct value has {} field(s)",
tys.len(),
value_num_fields
);
TupleObject { tys: tys.clone(), value }
}
/// Convenience function. Create a [`TupleObject`] from an iterator of objects.
pub fn from_objects<I, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
objects: I,
) -> Self
where
I: IntoIterator<Item = AnyObject<'ctx>>,
{
let (values, tys): (Vec<_>, Vec<_>) =
objects.into_iter().map(|object| (object.value, object.ty)).unzip();
let llvm_tys = tys.iter().map(|ty| ctx.get_llvm_type(generator, *ty)).collect_vec();
let llvm_tuple_ty = ctx.ctx.struct_type(&llvm_tys, false);
let pllvm_tuple = ctx.builder.build_alloca(llvm_tuple_ty, "tuple").unwrap();
for (i, val) in values.into_iter().enumerate() {
let pval = ctx.builder.build_struct_gep(pllvm_tuple, i as u32, "value").unwrap();
ctx.builder.build_store(pval, val).unwrap();
}
let value = ctx.builder.build_load(pllvm_tuple, "").unwrap().into_struct_value();
TupleObject { tys, value }
}
#[must_use]
pub fn num_elements(&self) -> usize {
self.tys.len()
}
/// Get the `len()` of this tuple.
#[must_use]
pub fn len<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<SizeT>> {
Int(SizeT).const_int(generator, ctx.ctx, self.num_elements() as u64)
}
/// Get the `i`-th (0-based) object in this tuple.
pub fn index(&self, ctx: &mut CodeGenContext<'ctx, '_>, i: usize) -> AnyObject<'ctx> {
assert!(
i < self.num_elements(),
"Tuple object with length {} have index {i}",
self.num_elements()
);
let value = ctx.builder.build_extract_value(self.value, i as u32, "tuple[{i}]").unwrap();
let ty = self.tys[i];
AnyObject { ty, value }
}
}

335
nac3core/src/codegen/stmt.rs
View File

@ -1,24 +1,13 @@
use super::{
super::symbol_resolver::ValueEnum,
classes::{ArrayLikeIndexer, ArraySliceValue, ListValue, RangeValue},
expr::{destructure_range, gen_binop_expr},
gen_in_range_check,
irrt::{handle_slice_indices, list_slice_assignment},
model::*,
object::{
any::AnyObject,
exception::Exception,
ndarray::{
indexing::util::gen_ndarray_subscript_ndindices, NDArrayObject, ScalarOrNDArray,
},
range::RangeObject,
str::str_model,
},
macros::codegen_unreachable,
CodeGenContext, CodeGenerator,
};
use crate::{
codegen::{
classes::{ArrayLikeIndexer, ArraySliceValue, ListValue},
expr::gen_binop_expr,
gen_in_range_check,
},
symbol_resolver::ValueEnum,
toplevel::{DefinitionId, TopLevelDef},
typecheck::{
magic_methods::Binop,
@ -33,7 +22,9 @@ use inkwell::{
IntPredicate,
};
use itertools::{izip, Itertools};
use nac3parser::ast::{ExcepthandlerKind, Expr, ExprKind, Location, Stmt, StmtKind, StrRef};
use nac3parser::ast::{
Constant, ExcepthandlerKind, Expr, ExprKind, Location, Stmt, StmtKind, StrRef,
};
/// See [`CodeGenerator::gen_var_alloc`].
pub fn gen_var<'ctx>(
@ -128,7 +119,7 @@ pub fn gen_store_target<'ctx, G: CodeGenerator>(
return Ok(None);
};
let BasicValueEnum::PointerValue(ptr) = val else {
unreachable!();
codegen_unreachable!(ctx);
};
unsafe {
ctx.builder.build_in_bounds_gep(
@ -142,7 +133,7 @@ pub fn gen_store_target<'ctx, G: CodeGenerator>(
}
.unwrap()
}
_ => unreachable!(),
_ => codegen_unreachable!(ctx),
}))
}
@ -183,6 +174,14 @@ pub fn gen_assign<'ctx, G: CodeGenerator>(
}
}
let val = value.to_basic_value_enum(ctx, generator, target.custom.unwrap())?;
// Perform i1 <-> i8 conversion as needed
let val = if ctx.unifier.unioned(target.custom.unwrap(), ctx.primitives.bool) {
generator.bool_to_i8(ctx, val.into_int_value()).into()
} else {
val
};
ctx.builder.build_store(ptr, val).unwrap();
}
};
@ -200,12 +199,12 @@ pub fn gen_assign_target_list<'ctx, G: CodeGenerator>(
// Deconstruct the tuple `value`
let BasicValueEnum::StructValue(tuple) = value.to_basic_value_enum(ctx, generator, value_ty)?
else {
unreachable!()
codegen_unreachable!(ctx)
};
// NOTE: Currently, RHS's type is forced to be a Tuple by the type inferencer.
let TypeEnum::TTuple { ty: tuple_tys, .. } = &*ctx.unifier.get_ty(value_ty) else {
unreachable!();
codegen_unreachable!(ctx);
};
assert_eq!(tuple.get_type().count_fields() as usize, tuple_tys.len());
@ -265,7 +264,7 @@ pub fn gen_assign_target_list<'ctx, G: CodeGenerator>(
// Now assign with that sub-tuple to the starred target.
generator.gen_assign(ctx, target, ValueEnum::Dynamic(sub_tuple_val), sub_tuple_ty)?;
} else {
unreachable!() // The typechecker ensures this
codegen_unreachable!(ctx) // The typechecker ensures this
}
// Handle assignment after the starred target
@ -313,7 +312,9 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
if let ExprKind::Slice { .. } = &key.node {
// Handle assigning to a slice
let ExprKind::Slice { lower, upper, step } = &key.node else { unreachable!() };
let ExprKind::Slice { lower, upper, step } = &key.node else {
codegen_unreachable!(ctx)
};
let Some((start, end, step)) = handle_slice_indices(
lower,
upper,
@ -408,47 +409,7 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
{
// Handle NDArray item assignment
// Process target
let target = generator
.gen_expr(ctx, target)?
.unwrap()
.to_basic_value_enum(ctx, generator, target_ty)?;
let target = AnyObject { value: target, ty: target_ty };
// Process key
let key = gen_ndarray_subscript_ndindices(generator, ctx, key)?;
// Process value
let value = value.to_basic_value_enum(ctx, generator, value_ty)?;
let value = AnyObject { value, ty: value_ty };
/*
Reference code:
```python
target = target[key]
value = np.asarray(value)
shape = np.broadcast_shape((target, value))
target = np.broadcast_to(target, shape)
value = np.broadcast_to(value, shape)
...and finally copy 1-1 from value to target.
```
*/
let target = NDArrayObject::from_object(generator, ctx, target);
let target = target.index(generator, ctx, &key);
let value =
ScalarOrNDArray::split_object(generator, ctx, value).to_ndarray(generator, ctx);
let broadcast_result = NDArrayObject::broadcast(generator, ctx, &[target, value]);
let target = broadcast_result.ndarrays[0];
let value = broadcast_result.ndarrays[1];
target.copy_data_from(generator, ctx, value);
todo!("ndarray subscript assignment is not yet implemented");
}
_ => {
panic!("encountered unknown target type: {}", ctx.unifier.stringify(target_ty));
@ -463,7 +424,9 @@ pub fn gen_for<G: CodeGenerator>(
ctx: &mut CodeGenContext<'_, '_>,
stmt: &Stmt<Option<Type>>,
) -> Result<(), String> {
let StmtKind::For { iter, target, body, orelse, .. } = &stmt.node else { unreachable!() };
let StmtKind::For { iter, target, body, orelse, .. } = &stmt.node else {
codegen_unreachable!(ctx)
};
// var_assignment static values may be changed in another branch
// if so, remove the static value as it may not be correct in this branch
@ -498,22 +461,16 @@ pub fn gen_for<G: CodeGenerator>(
TypeEnum::TObj { obj_id, .. }
if *obj_id == ctx.primitives.range.obj_id(&ctx.unifier).unwrap() =>
{
let range = AnyObject { value: iter_val, ty: iter_ty };
let range = RangeObject::from_object(generator, ctx, range);
let (start, stop, step) = range.instance.destructure(generator, ctx);
let start = start.value;
let stop = stop.value;
let step = step.value;
let iter_val = RangeValue::from_ptr_val(iter_val.into_pointer_value(), Some("range"));
// Internal variable for loop; Cannot be assigned
let i = generator.gen_var_alloc(ctx, int32.into(), Some("for.i.addr"))?;
// Variable declared in "target" expression of the loop; Can be reassigned *or* shadowed
let Some(target_i) =
generator.gen_store_target(ctx, target, Some("for.target.addr"))?
else {
unreachable!()
codegen_unreachable!(ctx)
};
let (start, stop, step) = destructure_range(ctx, iter_val);
ctx.builder.build_store(i, start).unwrap();
@ -954,7 +911,7 @@ pub fn gen_while<G: CodeGenerator>(
ctx: &mut CodeGenContext<'_, '_>,
stmt: &Stmt<Option<Type>>,
) -> Result<(), String> {
let StmtKind::While { test, body, orelse, .. } = &stmt.node else { unreachable!() };
let StmtKind::While { test, body, orelse, .. } = &stmt.node else { codegen_unreachable!(ctx) };
// var_assignment static values may be changed in another branch
// if so, remove the static value as it may not be correct in this branch
@ -984,7 +941,7 @@ pub fn gen_while<G: CodeGenerator>(
return Ok(());
};
let BasicValueEnum::IntValue(test) = test else { unreachable!() };
let BasicValueEnum::IntValue(test) = test else { codegen_unreachable!(ctx) };
ctx.builder
.build_conditional_branch(generator.bool_to_i1(ctx, test), body_bb, orelse_bb)
@ -1132,7 +1089,7 @@ pub fn gen_if<G: CodeGenerator>(
ctx: &mut CodeGenContext<'_, '_>,
stmt: &Stmt<Option<Type>>,
) -> Result<(), String> {
let StmtKind::If { test, body, orelse, .. } = &stmt.node else { unreachable!() };
let StmtKind::If { test, body, orelse, .. } = &stmt.node else { codegen_unreachable!(ctx) };
// var_assignment static values may be changed in another branch
// if so, remove the static value as it may not be correct in this branch
@ -1248,58 +1205,65 @@ pub fn exn_constructor<'ctx>(
mut args: Vec<(Option<StrRef>, ValueEnum<'ctx>)>,
generator: &mut dyn CodeGenerator,
) -> Result<Option<BasicValueEnum<'ctx>>, String> {
let (exn_ty, exn) = obj.unwrap();
let exn = exn.to_basic_value_enum(ctx, generator, exn_ty)?;
let exn = Ptr(Struct(Exception)).check_value(generator, ctx.ctx, exn).unwrap();
// Get the Exception name `exn_name` of this Exception object.
let exn_def_id = if let TypeEnum::TObj { obj_id, .. } = &*ctx.unifier.get_ty(exn_ty) {
let (zelf_ty, zelf) = obj.unwrap();
let zelf = zelf.to_basic_value_enum(ctx, generator, zelf_ty)?.into_pointer_value();
let int32 = ctx.ctx.i32_type();
let zero = int32.const_zero();
let zelf_id = if let TypeEnum::TObj { obj_id, .. } = &*ctx.unifier.get_ty(zelf_ty) {
obj_id.0
} else {
unreachable!()
codegen_unreachable!(ctx)
};
let defs = ctx.top_level.definitions.read();
let exn_def = defs[exn_def_id].read();
let TopLevelDef::Class { name: exn_name, .. } = &*exn_def else { unreachable!() };
let exn_name = format!("{}:{}", ctx.resolver.get_exception_id(exn_def_id), exn_name);
// Initialize the fields of the Exception object.
let empty_str = ctx.gen_string(generator, "");
let num_0 = Int(Int32).const_0(generator, ctx.ctx);
// Initialize `self.id`.
let id = ctx.resolver.get_string_id(&exn_name);
let id = Int(Int32).const_int(generator, ctx.ctx, id as u64);
exn.set(ctx, |f| f.id, id);
// Initialize `self.msg`.
let def = defs[zelf_id].read();
let TopLevelDef::Class { name: zelf_name, .. } = &*def else { codegen_unreachable!(ctx) };
let exception_name = format!("{}:{}", ctx.resolver.get_exception_id(zelf_id), zelf_name);
unsafe {
let id_ptr = ctx.builder.build_in_bounds_gep(zelf, &[zero, zero], "exn.id").unwrap();
let id = ctx.resolver.get_string_id(&exception_name);
ctx.builder.build_store(id_ptr, int32.const_int(id as u64, false)).unwrap();
let empty_string =
ctx.gen_const(generator, &Constant::Str(String::new()), ctx.primitives.str);
let ptr = ctx
.builder
.build_in_bounds_gep(zelf, &[zero, int32.const_int(5, false)], "exn.msg")
.unwrap();
let msg = if args.is_empty() {
// Default to `msg` to "" if the user didn't pass anything.
empty_str
empty_string.unwrap()
} else {
let msg = args.remove(0).1.to_basic_value_enum(ctx, generator, ctx.primitives.str)?;
str_model().check_value(generator, ctx.ctx, msg).unwrap()
args.remove(0).1.to_basic_value_enum(ctx, generator, ctx.primitives.str)?
};
exn.set(ctx, |f| f.msg, msg);
// Initialize `self.params`, the arguments after `msg` are the params.
for (i, (_, param)) in args.into_iter().enumerate() {
assert!(i <= 3, "There should only be at most 3 exception parameters");
let param = param.to_basic_value_enum(ctx, generator, ctx.primitives.int64)?;
let param = Int(Int64).check_value(generator, ctx.ctx, param).unwrap();
exn.set(ctx, |f| f.params[i], param);
ctx.builder.build_store(ptr, msg).unwrap();
for i in &[6, 7, 8] {
let value = if args.is_empty() {
ctx.ctx.i64_type().const_zero().into()
} else {
args.remove(0).1.to_basic_value_enum(ctx, generator, ctx.primitives.int64)?
};
let ptr = ctx
.builder
.build_in_bounds_gep(zelf, &[zero, int32.const_int(*i, false)], "exn.param")
.unwrap();
ctx.builder.build_store(ptr, value).unwrap();
}
// Initialize everything else to 0 or "".
exn.set(ctx, |f| f.line, num_0);
exn.set(ctx, |f| f.column, num_0);
exn.set(ctx, |f| f.function, empty_str);
exn.set(ctx, |f| f.filename, empty_str);
Ok(Some(exn.value.into()))
// set file, func to empty string
for i in &[1, 4] {
let ptr = ctx
.builder
.build_in_bounds_gep(zelf, &[zero, int32.const_int(*i, false)], "exn.str")
.unwrap();
ctx.builder.build_store(ptr, empty_string.unwrap()).unwrap();
}
// set ints to zero
for i in &[2, 3] {
let ptr = ctx
.builder
.build_in_bounds_gep(zelf, &[zero, int32.const_int(*i, false)], "exn.ints")
.unwrap();
ctx.builder.build_store(ptr, zero).unwrap();
}
}
Ok(Some(zelf.into()))
}
/// Generates IR for a `raise` statement.
@ -1309,27 +1273,43 @@ pub fn exn_constructor<'ctx>(
pub fn gen_raise<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
exception: Option<Instance<'ctx, Ptr<Struct<Exception>>>>,
exception: Option<&BasicValueEnum<'ctx>>,
loc: Location,
) {
if let Some(exn) = exception {
let filename = loc.file.0;
let filename = ctx.gen_string(generator, filename);
exn.set(ctx, |f| f.filename, filename);
if let Some(exception) = exception {
unsafe {
let int32 = ctx.ctx.i32_type();
let zero = int32.const_zero();
let exception = exception.into_pointer_value();
let file_ptr = ctx
.builder
.build_in_bounds_gep(exception, &[zero, int32.const_int(1, false)], "file_ptr")
.unwrap();
let filename = ctx.gen_string(generator, loc.file.0);
ctx.builder.build_store(file_ptr, filename).unwrap();
let row_ptr = ctx
.builder
.build_in_bounds_gep(exception, &[zero, int32.const_int(2, false)], "row_ptr")
.unwrap();
ctx.builder.build_store(row_ptr, int32.const_int(loc.row as u64, false)).unwrap();
let col_ptr = ctx
.builder
.build_in_bounds_gep(exception, &[zero, int32.const_int(3, false)], "col_ptr")
.unwrap();
ctx.builder.build_store(col_ptr, int32.const_int(loc.column as u64, false)).unwrap();
let row = Int(Int32).const_int(generator, ctx.ctx, loc.row as u64);
exn.set(ctx, |f| f.line, row);
let column = Int(Int32).const_int(generator, ctx.ctx, loc.column as u64);
exn.set(ctx, |f| f.column, column);
let current_fn = ctx.builder.get_insert_block().unwrap().get_parent().unwrap();
let current_fn_name = current_fn.get_name().to_str().unwrap();
let current_fn_name = ctx.gen_string(generator, current_fn_name);
exn.set(ctx, |f| f.function, current_fn_name);
let current_fun = ctx.builder.get_insert_block().unwrap().get_parent().unwrap();
let fun_name = ctx.gen_string(generator, current_fun.get_name().to_str().unwrap());
let name_ptr = ctx
.builder
.build_in_bounds_gep(exception, &[zero, int32.const_int(4, false)], "name_ptr")
.unwrap();
ctx.builder.build_store(name_ptr, fun_name).unwrap();
}
let raise = get_builtins(generator, ctx, "__nac3_raise");
ctx.build_call_or_invoke(raise, &[exn.value.into()], "raise");
let exception = *exception;
ctx.build_call_or_invoke(raise, &[exception], "raise");
} else {
let resume = get_builtins(generator, ctx, "__nac3_resume");
ctx.build_call_or_invoke(resume, &[], "resume");
@ -1344,7 +1324,7 @@ pub fn gen_try<'ctx, 'a, G: CodeGenerator>(
target: &Stmt<Option<Type>>,
) -> Result<(), String> {
let StmtKind::Try { body, handlers, orelse, finalbody, .. } = &target.node else {
unreachable!()
codegen_unreachable!(ctx)
};
// if we need to generate anything related to exception, we must have personality defined
@ -1421,7 +1401,7 @@ pub fn gen_try<'ctx, 'a, G: CodeGenerator>(
if let TypeEnum::TObj { obj_id, .. } = &*ctx.unifier.get_ty(type_.custom.unwrap()) {
*obj_id
} else {
unreachable!()
codegen_unreachable!(ctx)
};
let exception_name = format!("{}:{}", ctx.resolver.get_exception_id(obj_id.0), exn_name);
let exn_id = ctx.resolver.get_string_id(&exception_name);
@ -1693,6 +1673,23 @@ pub fn gen_return<G: CodeGenerator>(
} else {
None
};
// Remap boolean return type into i1
let value = value.map(|ret_val| {
// The "return type" of a sret function is in the first parameter
let expected_ty = if ctx.need_sret {
func.get_type().get_param_types()[0]
} else {
func.get_type().get_return_type().unwrap()
};
if matches!(expected_ty, BasicTypeEnum::IntType(ty) if ty.get_bit_width() == 1) {
generator.bool_to_i1(ctx, ret_val.into_int_value()).into()
} else {
ret_val
}
});
if let Some(return_target) = ctx.return_target {
if let Some(value) = value {
ctx.builder.build_store(ctx.return_buffer.unwrap(), value).unwrap();
@ -1703,25 +1700,6 @@ pub fn gen_return<G: CodeGenerator>(
ctx.builder.build_store(ctx.return_buffer.unwrap(), value.unwrap()).unwrap();
ctx.builder.build_return(None).unwrap();
} else {
// Remap boolean return type into i1
let value = value.map(|v| {
let expected_ty = func.get_type().get_return_type().unwrap();
let ret_val = v.as_basic_value_enum();
if expected_ty.is_int_type() && ret_val.is_int_value() {
let ret_type = expected_ty.into_int_type();
let ret_val = ret_val.into_int_value();
if ret_type.get_bit_width() == 1 && ret_val.get_type().get_bit_width() != 1 {
generator.bool_to_i1(ctx, ret_val)
} else {
ret_val
}
.into()
} else {
ret_val
}
});
let value = value.as_ref().map(|v| v as &dyn BasicValue);
ctx.builder.build_return(value).unwrap();
}
@ -1790,14 +1768,35 @@ pub fn gen_stmt<G: CodeGenerator>(
StmtKind::Try { .. } => gen_try(generator, ctx, stmt)?,
StmtKind::Raise { exc, .. } => {
if let Some(exc) = exc {
let exc = if let Some(v) = generator.gen_expr(ctx, exc)? {
let exn = if let ExprKind::Name { id, .. } = &exc.node {
// Handle "raise Exception" short form
let def_id = ctx.resolver.get_identifier_def(*id).map_err(|e| {
format!("{} (at {})", e.iter().next().unwrap(), exc.location)
})?;
let def = ctx.top_level.definitions.read();
let TopLevelDef::Class { constructor, .. } = *def[def_id.0].read() else {
return Err(format!("Failed to resolve symbol {id} (at {})", exc.location));
};
let TypeEnum::TFunc(signature) =
ctx.unifier.get_ty(constructor.unwrap()).as_ref().clone()
else {
return Err(format!("Failed to resolve symbol {id} (at {})", exc.location));
};
generator
.gen_call(ctx, None, (&signature, def_id), Vec::default())?
.map(Into::into)
} else {
generator.gen_expr(ctx, exc)?
};
let exc = if let Some(v) = exn {
v.to_basic_value_enum(ctx, generator, exc.custom.unwrap())?
} else {
return Ok(());
};
let exc = Ptr(Struct(Exception)).check_value(generator, ctx.ctx, exc).unwrap();
gen_raise(generator, ctx, Some(exc), stmt.location);
gen_raise(generator, ctx, Some(&exc), stmt.location);
} else {
gen_raise(generator, ctx, None, stmt.location);
}
@ -1810,19 +1809,13 @@ pub fn gen_stmt<G: CodeGenerator>(
};
let err_msg = match msg {
Some(msg) => {
let msg_ty = msg.custom.unwrap();
if let Some(msg) = generator.gen_expr(ctx, msg)? {
let msg = msg.to_basic_value_enum(ctx, generator, msg_ty)?;
let msg = str_model().check_value(generator, ctx.ctx, msg).unwrap();
msg
if let Some(v) = generator.gen_expr(ctx, msg)? {
v.to_basic_value_enum(ctx, generator, msg.custom.unwrap())?
} else {
return Ok(());
}
}
None => {
// Return an empty string.
ctx.gen_string(generator, "")
}
None => ctx.gen_string(generator, "").into(),
};
ctx.make_assert_impl(
generator,

22
nac3core/src/codegen/test.rs
View File

@ -1,6 +1,6 @@
use crate::{
codegen::{
classes::{ListType, ProxyType},
classes::{ListType, NDArrayType, ProxyType, RangeType},
concrete_type::ConcreteTypeStore,
CodeGenContext, CodeGenLLVMOptions, CodeGenTargetMachineOptions, CodeGenTask,
CodeGenerator, DefaultCodeGenerator, WithCall, WorkerRegistry,
@ -448,3 +448,23 @@ fn test_classes_list_type_new() {
let llvm_list = ListType::new(&generator, &ctx, llvm_i32.into());
assert!(ListType::is_type(llvm_list.as_base_type(), llvm_usize).is_ok());
}
#[test]
fn test_classes_range_type_new() {
let ctx = inkwell::context::Context::create();
let llvm_range = RangeType::new(&ctx);
assert!(RangeType::is_type(llvm_range.as_base_type()).is_ok());
}
#[test]
fn test_classes_ndarray_type_new() {
let ctx = inkwell::context::Context::create();
let generator = DefaultCodeGenerator::new(String::new(), 64);
let llvm_i32 = ctx.i32_type();
let llvm_usize = generator.get_size_type(&ctx);
let llvm_ndarray = NDArrayType::new(&generator, &ctx, llvm_i32.into());
assert!(NDArrayType::is_type(llvm_ndarray.as_base_type(), llvm_usize).is_ok());
}

4
nac3core/src/lib.rs
View File

@ -19,6 +19,10 @@
clippy::wildcard_imports
)]
// users of nac3core need to use the same version of these dependencies, so expose them as nac3core::*
pub use inkwell;
pub use nac3parser;
pub mod codegen;
pub mod symbol_resolver;
pub mod toplevel;

260
nac3core/src/toplevel/builtins.rs
View File

@ -1,6 +1,6 @@
use std::iter::once;
use helper::{debug_assert_prim_is_allowed, extract_ndims, make_exception_fields, PrimDefDetails};
use helper::{debug_assert_prim_is_allowed, make_exception_fields, PrimDefDetails};
use indexmap::IndexMap;
use inkwell::{
attributes::{Attribute, AttributeLoc},
@ -9,19 +9,13 @@ use inkwell::{
IntPredicate,
};
use itertools::Either;
use numpy::unpack_ndarray_var_tys;
use strum::IntoEnumIterator;
use crate::{
codegen::{
builtin_fns,
model::*,
classes::{ProxyValue, RangeValue},
numpy::*,
object::{
any::AnyObject,
ndarray::{shape_util::parse_numpy_int_sequence, NDArrayObject},
range::RangeObject,
},
stmt::exn_constructor,
},
symbol_resolver::SymbolValue,
@ -517,14 +511,6 @@ impl<'a> BuiltinBuilder<'a> {
| PrimDef::FunNpEye
| PrimDef::FunNpIdentity => self.build_ndarray_other_factory_function(prim),
PrimDef::FunNpSize | PrimDef::FunNpShape | PrimDef::FunNpStrides => {
self.build_ndarray_property_getter_function(prim)
}
PrimDef::FunNpBroadcastTo | PrimDef::FunNpTranspose | PrimDef::FunNpReshape => {
self.build_ndarray_view_function(prim)
}
PrimDef::FunStr => self.build_str_function(),
PrimDef::FunFloor | PrimDef::FunFloor64 | PrimDef::FunCeil | PrimDef::FunCeil64 => {
@ -590,6 +576,10 @@ impl<'a> BuiltinBuilder<'a> {
| PrimDef::FunNpHypot
| PrimDef::FunNpNextAfter => self.build_np_2ary_function(prim),
PrimDef::FunNpTranspose | PrimDef::FunNpReshape => {
self.build_np_sp_ndarray_function(prim)
}
PrimDef::FunNpDot
| PrimDef::FunNpLinalgCholesky
| PrimDef::FunNpLinalgQr
@ -717,10 +707,9 @@ impl<'a> BuiltinBuilder<'a> {
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|ctx, obj, _, args, generator| {
let (zelf_ty, zelf) = obj.unwrap();
let zelf = zelf.to_basic_value_enum(ctx, generator, zelf_ty)?;
let zelf = AnyObject { ty: zelf_ty, value: zelf };
let zelf = RangeObject::from_object(generator, ctx, zelf);
let zelf =
zelf.to_basic_value_enum(ctx, generator, zelf_ty)?.into_pointer_value();
let zelf = RangeValue::from_ptr_val(zelf, Some("range"));
let mut start = None;
let mut stop = None;
@ -803,14 +792,11 @@ impl<'a> BuiltinBuilder<'a> {
});
let start = start.unwrap_or_else(|| int32.const_zero());
let start = Int(Int32).believe_value(start);
let stop = Int(Int32).believe_value(stop);
let step = Int(Int32).believe_value(step);
zelf.instance.start(ctx).store(ctx, start);
zelf.instance.stop(ctx).store(ctx, stop);
zelf.instance.step(ctx).store(ctx, step);
zelf.store_start(ctx, start);
zelf.store_end(ctx, stop);
zelf.store_step(ctx, step);
Ok(Some(zelf.instance.value.as_basic_value_enum()))
Ok(Some(zelf.as_base_value().into()))
},
)))),
loc: None,
@ -1399,171 +1385,6 @@ impl<'a> BuiltinBuilder<'a> {
}
}
fn build_ndarray_property_getter_function(&mut self, prim: PrimDef) -> TopLevelDef {
debug_assert_prim_is_allowed(
prim,
&[PrimDef::FunNpSize, PrimDef::FunNpShape, PrimDef::FunNpStrides],
);
let in_ndarray_ty = self.unifier.get_fresh_var_with_range(
&[self.primitives.ndarray],
Some("T".into()),
None,
);
match prim {
PrimDef::FunNpSize => create_fn_by_codegen(
self.unifier,
&into_var_map([in_ndarray_ty]),
prim.name(),
self.primitives.int32,
&[(in_ndarray_ty.ty, "a")],
Box::new(|ctx, obj, fun, args, generator| {
assert!(obj.is_none());
assert_eq!(args.len(), 1);
let ndarray_ty = fun.0.args[0].ty;
let ndarray =
args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
let ndarray = AnyObject { ty: ndarray_ty, value: ndarray };
let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
let size =
ndarray.size(generator, ctx).truncate_or_bit_cast(generator, ctx, Int32);
Ok(Some(size.value.as_basic_value_enum()))
}),
),
PrimDef::FunNpShape | PrimDef::FunNpStrides => {
// The function signatures of `np_shape` an `np_size` are the same.
// Mixed together for convenience.
// The return type is a tuple of variable length depending on the ndims of the input ndarray.
let ret_ty = self.unifier.get_dummy_var().ty; // Handled by special folding
create_fn_by_codegen(
self.unifier,
&into_var_map([in_ndarray_ty]),
prim.name(),
ret_ty,
&[(in_ndarray_ty.ty, "a")],
Box::new(move |ctx, obj, fun, args, generator| {
assert!(obj.is_none());
assert_eq!(args.len(), 1);
let ndarray_ty = fun.0.args[0].ty;
let ndarray =
args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
let ndarray = AnyObject { ty: ndarray_ty, value: ndarray };
let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
let result_tuple = match prim {
PrimDef::FunNpShape => ndarray.make_shape_tuple(generator, ctx),
PrimDef::FunNpStrides => ndarray.make_strides_tuple(generator, ctx),
_ => unreachable!(),
};
Ok(Some(result_tuple.value.as_basic_value_enum()))
}),
)
}
_ => unreachable!(),
}
}
/// Build np/sp functions that take as input `NDArray` only
fn build_ndarray_view_function(&mut self, prim: PrimDef) -> TopLevelDef {
debug_assert_prim_is_allowed(
prim,
&[PrimDef::FunNpBroadcastTo, PrimDef::FunNpTranspose, PrimDef::FunNpReshape],
);
let in_ndarray_ty = self.unifier.get_fresh_var_with_range(
&[self.primitives.ndarray],
Some("T".into()),
None,
);
match prim {
PrimDef::FunNpTranspose => {
create_fn_by_codegen(
self.unifier,
&into_var_map([in_ndarray_ty]),
prim.name(),
in_ndarray_ty.ty,
&[(in_ndarray_ty.ty, "x")],
Box::new(move |ctx, _, fun, args, generator| {
let arg_ty = fun.0.args[0].ty;
let arg_val =
args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
let arg = AnyObject { ty: arg_ty, value: arg_val };
let ndarray = NDArrayObject::from_object(generator, ctx, arg);
let ndarray = ndarray.transpose(generator, ctx, None); // TODO: Add axes argument
Ok(Some(ndarray.instance.value.as_basic_value_enum()))
}),
)
}
// NOTE: on `ndarray_factory_fn_shape_arg_tvar` and
// the `param_ty` for `create_fn_by_codegen`.
//
// Similar to `build_ndarray_from_shape_factory_function` we delegate the responsibility of typechecking
// to [`typecheck::type_inferencer::Inferencer::fold_numpy_function_call_shape_argument`],
// and use a dummy [`TypeVar`] `ndarray_factory_fn_shape_arg_tvar` as a placeholder for `param_ty`.
PrimDef::FunNpBroadcastTo | PrimDef::FunNpReshape => {
// These two functions have the same function signature.
// Mixed together for convenience.
let ret_ty = self.unifier.get_dummy_var().ty; // Handled by special holding
create_fn_by_codegen(
self.unifier,
&VarMap::new(),
prim.name(),
ret_ty,
&[
(in_ndarray_ty.ty, "x"),
(self.ndarray_factory_fn_shape_arg_tvar.ty, "shape"), // Handled by special folding
],
Box::new(move |ctx, _, fun, args, generator| {
let ndarray_ty = fun.0.args[0].ty;
let ndarray_val =
args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
let shape_ty = fun.0.args[1].ty;
let shape_val =
args[1].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
let ndarray = AnyObject { value: ndarray_val, ty: ndarray_ty };
let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
let shape = AnyObject { value: shape_val, ty: shape_ty };
let (_, shape) = parse_numpy_int_sequence(generator, ctx, shape);
// The ndims after reshaping is gotten from the return type of the call.
let (_, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, fun.0.ret);
let ndims = extract_ndims(&ctx.unifier, ndims);
let new_ndarray = match prim {
PrimDef::FunNpBroadcastTo => {
ndarray.broadcast_to(generator, ctx, ndims, shape)
}
PrimDef::FunNpReshape => {
ndarray.reshape_or_copy(generator, ctx, ndims, shape)
}
_ => unreachable!(),
};
Ok(Some(new_ndarray.instance.value.as_basic_value_enum()))
}),
)
}
_ => unreachable!(),
}
}
/// Build the `str()` function.
fn build_str_function(&mut self) -> TopLevelDef {
let prim = PrimDef::FunStr;
@ -2051,6 +1872,57 @@ impl<'a> BuiltinBuilder<'a> {
}
}
/// Build np/sp functions that take as input `NDArray` only
fn build_np_sp_ndarray_function(&mut self, prim: PrimDef) -> TopLevelDef {
debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpTranspose, PrimDef::FunNpReshape]);
match prim {
PrimDef::FunNpTranspose => {
let ndarray_ty = self.unifier.get_fresh_var_with_range(
&[self.ndarray_num_ty],
Some("T".into()),
None,
);
create_fn_by_codegen(
self.unifier,
&into_var_map([ndarray_ty]),
prim.name(),
ndarray_ty.ty,
&[(ndarray_ty.ty, "x")],
Box::new(move |ctx, _, fun, args, generator| {
let arg_ty = fun.0.args[0].ty;
let arg_val =
args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
Ok(Some(ndarray_transpose(generator, ctx, (arg_ty, arg_val))?))
}),
)
}
// NOTE: on `ndarray_factory_fn_shape_arg_tvar` and
// the `param_ty` for `create_fn_by_codegen`.
//
// Similar to `build_ndarray_from_shape_factory_function` we delegate the responsibility of typechecking
// to [`typecheck::type_inferencer::Inferencer::fold_numpy_function_call_shape_argument`],
// and use a dummy [`TypeVar`] `ndarray_factory_fn_shape_arg_tvar` as a placeholder for `param_ty`.
PrimDef::FunNpReshape => create_fn_by_codegen(
self.unifier,
&VarMap::new(),
prim.name(),
self.ndarray_num_ty,
&[(self.ndarray_num_ty, "x"), (self.ndarray_factory_fn_shape_arg_tvar.ty, "shape")],
Box::new(move |ctx, _, fun, args, generator| {
let x1_ty = fun.0.args[0].ty;
let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
let x2_ty = fun.0.args[1].ty;
let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
Ok(Some(ndarray_reshape(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
}),
),
_ => unreachable!(),
}
}
/// Build `np_linalg` and `sp_linalg` functions
///
/// The input to these functions must be floating point `NDArray`
@ -2082,12 +1954,10 @@ impl<'a> BuiltinBuilder<'a> {
Box::new(move |ctx, _, fun, args, generator| {
let x1_ty = fun.0.args[0].ty;
let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
let x2_ty = fun.0.args[1].ty;
let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
let result = ndarray_dot(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?;
Ok(Some(result))
Ok(Some(ndarray_dot(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
}),
),

22
nac3core/src/toplevel/composer.rs
View File

@ -23,7 +23,7 @@ impl Default for ComposerConfig {
}
}
type DefAst = (Arc<RwLock<TopLevelDef>>, Option<Stmt<()>>);
pub type DefAst = (Arc<RwLock<TopLevelDef>>, Option<Stmt<()>>);
pub struct TopLevelComposer {
// list of top level definitions, same as top level context
pub definition_ast_list: Vec<DefAst>,
@ -1822,7 +1822,12 @@ impl TopLevelComposer {
if *name != init_str_id {
unreachable!("must be init function here")
}
let all_inited = Self::get_all_assigned_field(body.as_slice())?;
let all_inited = Self::get_all_assigned_field(
object_id.0,
definition_ast_list,
body.as_slice(),
)?;
for (f, _, _) in fields {
if !all_inited.contains(f) {
return Err(HashSet::from([
@ -1889,7 +1894,8 @@ impl TopLevelComposer {
} = &mut *function_def
{
let signature_ty_enum = unifier.get_ty(*signature);
let TypeEnum::TFunc(FunSignature { args, ret, vars }) = signature_ty_enum.as_ref()
let TypeEnum::TFunc(FunSignature { args, ret, vars, .. }) =
signature_ty_enum.as_ref()
else {
unreachable!("must be typeenum::tfunc")
};
@ -2052,6 +2058,16 @@ impl TopLevelComposer {
instance_to_symbol.insert(String::new(), simple_name.to_string());
continue;
}
if !decorator_list.is_empty() {
if let ast::ExprKind::Call { func, .. } = &decorator_list[0].node {
if matches!(&func.node,
ast::ExprKind::Name{ id, .. } if id == &"rpc".into())
{
instance_to_symbol.insert(String::new(), simple_name.to_string());
continue;
}
}
}
let fun_body = body
.into_iter()

182
nac3core/src/toplevel/helper.rs
View File

@ -3,6 +3,7 @@ use std::convert::TryInto;
use crate::symbol_resolver::SymbolValue;
use crate::toplevel::numpy::unpack_ndarray_var_tys;
use crate::typecheck::typedef::{into_var_map, iter_type_vars, Mapping, TypeVarId, VarMap};
use ast::ExprKind;
use nac3parser::ast::{Constant, Location};
use strum::IntoEnumIterator;
use strum_macros::EnumIter;
@ -52,16 +53,6 @@ pub enum PrimDef {
FunNpEye,
FunNpIdentity,
// NumPy ndarray property getters
FunNpSize,
FunNpShape,
FunNpStrides,
// NumPy ndarray view functions
FunNpBroadcastTo,
FunNpTranspose,
FunNpReshape,
// Miscellaneous NumPy & SciPy functions
FunNpRound,
FunNpFloor,
@ -109,6 +100,8 @@ pub enum PrimDef {
FunNpLdExp,
FunNpHypot,
FunNpNextAfter,
FunNpTranspose,
FunNpReshape,
// Linalg functions
FunNpDot,
@ -246,16 +239,6 @@ impl PrimDef {
PrimDef::FunNpEye => fun("np_eye", None),
PrimDef::FunNpIdentity => fun("np_identity", None),
// NumPy NDArray property getters,
PrimDef::FunNpSize => fun("np_size", None),
PrimDef::FunNpShape => fun("np_shape", None),
PrimDef::FunNpStrides => fun("np_strides", None),
// NumPy NDArray view functions
PrimDef::FunNpBroadcastTo => fun("np_broadcast_to", None),
PrimDef::FunNpTranspose => fun("np_transpose", None),
PrimDef::FunNpReshape => fun("np_reshape", None),
// Miscellaneous NumPy & SciPy functions
PrimDef::FunNpRound => fun("np_round", None),
PrimDef::FunNpFloor => fun("np_floor", None),
@ -303,6 +286,8 @@ impl PrimDef {
PrimDef::FunNpLdExp => fun("np_ldexp", None),
PrimDef::FunNpHypot => fun("np_hypot", None),
PrimDef::FunNpNextAfter => fun("np_nextafter", None),
PrimDef::FunNpTranspose => fun("np_transpose", None),
PrimDef::FunNpReshape => fun("np_reshape", None),
// Linalg functions
PrimDef::FunNpDot => fun("np_dot", None),
@ -749,7 +734,16 @@ impl TopLevelComposer {
)
}
pub fn get_all_assigned_field(stmts: &[Stmt<()>]) -> Result<HashSet<StrRef>, HashSet<String>> {
/// This function returns the fields that have been initialized in the `__init__` function of a class
/// The function takes as input:
/// * `class_id`: The `object_id` of the class whose function is being evaluated (check `TopLevelDef::Class`)
/// * `definition_ast_list`: A list of ast definitions and statements defined in `TopLevelComposer`
/// * `stmts`: The body of function being parsed. Each statment is analyzed to check varaible initialization statements
pub fn get_all_assigned_field(
class_id: usize,
definition_ast_list: &Vec<DefAst>,
stmts: &[Stmt<()>],
) -> Result<HashSet<StrRef>, HashSet<String>> {
let mut result = HashSet::new();
for s in stmts {
match &s.node {
@ -785,30 +779,138 @@ impl TopLevelComposer {
// TODO: do not check for For and While?
ast::StmtKind::For { body, orelse, .. }
| ast::StmtKind::While { body, orelse, .. } => {
result.extend(Self::get_all_assigned_field(body.as_slice())?);
result.extend(Self::get_all_assigned_field(orelse.as_slice())?);
result.extend(Self::get_all_assigned_field(
class_id,
definition_ast_list,
body.as_slice(),
)?);
result.extend(Self::get_all_assigned_field(
class_id,
definition_ast_list,
orelse.as_slice(),
)?);
}
ast::StmtKind::If { body, orelse, .. } => {
let inited_for_sure = Self::get_all_assigned_field(body.as_slice())?
.intersection(&Self::get_all_assigned_field(orelse.as_slice())?)
let inited_for_sure = Self::get_all_assigned_field(
class_id,
definition_ast_list,
body.as_slice(),
)?
.intersection(&Self::get_all_assigned_field(
class_id,
definition_ast_list,
orelse.as_slice(),
)?)
.copied()
.collect::<HashSet<_>>();
result.extend(inited_for_sure);
}
ast::StmtKind::Try { body, orelse, finalbody, .. } => {
let inited_for_sure = Self::get_all_assigned_field(body.as_slice())?
.intersection(&Self::get_all_assigned_field(orelse.as_slice())?)
let inited_for_sure = Self::get_all_assigned_field(
class_id,
definition_ast_list,
body.as_slice(),
)?
.intersection(&Self::get_all_assigned_field(
class_id,
definition_ast_list,
orelse.as_slice(),
)?)
.copied()
.collect::<HashSet<_>>();
result.extend(inited_for_sure);
result.extend(Self::get_all_assigned_field(finalbody.as_slice())?);
result.extend(Self::get_all_assigned_field(
class_id,
definition_ast_list,
finalbody.as_slice(),
)?);
}
ast::StmtKind::With { body, .. } => {
result.extend(Self::get_all_assigned_field(body.as_slice())?);
result.extend(Self::get_all_assigned_field(
class_id,
definition_ast_list,
body.as_slice(),
)?);
}
// Variables Initialized in function calls
ast::StmtKind::Expr { value, .. } => {
let ExprKind::Call { func, .. } = &value.node else {
continue;
};
let ExprKind::Attribute { value, attr, .. } = &func.node else {
continue;
};
let ExprKind::Name { id, .. } = &value.node else {
continue;
};
// Need to consider the two cases:
// Case 1) Call to class function i.e. id = `self`
// Case 2) Call to class ancestor function i.e. id = ancestor_name
// We leave checking whether function in case 2 belonged to class ancestor or not to type checker
//
// According to current handling of `self`, function definition are fixed and do not change regardless
// of which object is passed as `self` i.e. virtual polymorphism is not supported
// Therefore, we change class id for case 2 to reflect behavior of our compiler
let class_name = if *id == "self".into() {
let ast::StmtKind::ClassDef { name, .. } =
&definition_ast_list[class_id].1.as_ref().unwrap().node
else {
unreachable!()
};
name
} else {
id
};
let parent_method = definition_ast_list.iter().find_map(|def| {
let (
class_def,
Some(ast::Located {
node: ast::StmtKind::ClassDef { name, body, .. },
..
}),
) = &def
else {
return None;
};
let TopLevelDef::Class { object_id: class_id, .. } = &*class_def.read()
else {
unreachable!()
};
if name == class_name {
body.iter().find_map(|m| {
let ast::StmtKind::FunctionDef { name, body, .. } = &m.node else {
return None;
};
if *name == *attr {
return Some((body.clone(), class_id.0));
}
None
})
} else {
None
}
});
// If method body is none then method does not exist
if let Some((method_body, class_id)) = parent_method {
result.extend(Self::get_all_assigned_field(
class_id,
definition_ast_list,
method_body.as_slice(),
)?);
} else {
return Err(HashSet::from([format!(
"{}.{} not found in class {class_name} at {}",
*id, *attr, value.location
)]));
}
}
ast::StmtKind::Pass { .. }
| ast::StmtKind::Assert { .. }
| ast::StmtKind::Expr { .. } => {}
| ast::StmtKind::AnnAssign { .. } => {}
_ => {
unimplemented!()
@ -1016,23 +1118,3 @@ pub fn arraylike_get_ndims(unifier: &mut Unifier, ty: Type) -> u64 {
_ => 0,
}
}
/// Extract an ndarray's `ndims` [type][`Type`] in `u64`. Panic if not possible.
/// The `ndims` must only contain 1 value.
#[must_use]
pub fn extract_ndims(unifier: &Unifier, ndims_ty: Type) -> u64 {
let ndims_ty_enum = unifier.get_ty_immutable(ndims_ty);
let TypeEnum::TLiteral { values, .. } = &*ndims_ty_enum else {
panic!("ndims_ty should be a TLiteral");
};
assert_eq!(values.len(), 1, "ndims_ty TLiteral should only contain 1 value");
let ndims = values[0].clone();
u64::try_from(ndims).unwrap()
}
/// Return an ndarray's `ndims` as a typechecker [`Type`] from its `u64` value.
pub fn create_ndims(unifier: &mut Unifier, ndims: u64) -> Type {
unifier.get_fresh_literal(vec![SymbolValue::U64(ndims)], None)
}

2
nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__generic_class.snap
View File

@ -5,7 +5,7 @@ expression: res_vec
[
"Class {\nname: \"Generic_A\",\nancestors: [\"Generic_A[V]\", \"B\"],\nfields: [\"aa\", \"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\"), (\"fun\", \"fn[[a:int32], V]\")],\ntype_vars: [\"V\"]\n}\n",
"Function {\nname: \"Generic_A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(257)]\n}\n",
"Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(241)]\n}\n",
"Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [\"aa\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\")],\ntype_vars: []\n}\n",
"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"B.foo\",\nsig: \"fn[[b:T], none]\",\nvar_id: []\n}\n",

2
nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__inheritance_override.snap
View File

@ -7,7 +7,7 @@ expression: res_vec
"Function {\nname: \"A.__init__\",\nsig: \"fn[[t:T], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
"Function {\nname: \"A.foo\",\nsig: \"fn[[c:C], none]\",\nvar_id: []\n}\n",
"Class {\nname: \"B\",\nancestors: [\"B[typevar246]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar246\"]\n}\n",
"Class {\nname: \"B\",\nancestors: [\"B[typevar230]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar230\"]\n}\n",
"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"B.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
"Class {\nname: \"C\",\nancestors: [\"C\", \"B[bool]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\", \"e\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: []\n}\n",

4
nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__list_tuple_generic.snap
View File

@ -5,8 +5,8 @@ expression: res_vec
[
"Function {\nname: \"foo\",\nsig: \"fn[[a:list[int32], b:tuple[T, float]], A[B, bool]]\",\nvar_id: []\n}\n",
"Class {\nname: \"A\",\nancestors: [\"A[T, V]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[v:V], none]\"), (\"fun\", \"fn[[a:T], V]\")],\ntype_vars: [\"T\", \"V\"]\n}\n",
"Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(259)]\n}\n",
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(264)]\n}\n",
"Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(243)]\n}\n",
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(248)]\n}\n",
"Function {\nname: \"gfun\",\nsig: \"fn[[a:A[list[float], int32]], none]\",\nvar_id: []\n}\n",
"Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [],\nmethods: [(\"__init__\", \"fn[[], none]\")],\ntype_vars: []\n}\n",
"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",

2
nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__self1.snap
View File

@ -3,7 +3,7 @@ source: nac3core/src/toplevel/test.rs
expression: res_vec
---
[
"Class {\nname: \"A\",\nancestors: [\"A[typevar245, typevar246]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar245\", \"typevar246\"]\n}\n",
"Class {\nname: \"A\",\nancestors: [\"A[typevar229, typevar230]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar229\", \"typevar230\"]\n}\n",
"Function {\nname: \"A.__init__\",\nsig: \"fn[[a:A[float, bool], b:B], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:A[float, bool]], A[bool, int32]]\",\nvar_id: []\n}\n",
"Class {\nname: \"B\",\nancestors: [\"B\", \"A[int64, bool]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\"), (\"foo\", \"fn[[b:B], B]\"), (\"bar\", \"fn[[a:A[list[B], int32]], tuple[A[virtual[A[B, int32]], bool], B]]\")],\ntype_vars: []\n}\n",

4
nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__simple_class_compose.snap
View File

@ -6,12 +6,12 @@ expression: res_vec
"Class {\nname: \"A\",\nancestors: [\"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
"Function {\nname: \"A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"A.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(265)]\n}\n",
"Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(249)]\n}\n",
"Class {\nname: \"B\",\nancestors: [\"B\", \"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
"Class {\nname: \"C\",\nancestors: [\"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
"Function {\nname: \"C.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"C.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"foo\",\nsig: \"fn[[a:A], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(273)]\n}\n",
"Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(257)]\n}\n",
]

93
nac3core/src/typecheck/magic_methods.rs
View File

@ -1,5 +1,5 @@
use crate::symbol_resolver::SymbolValue;
use crate::toplevel::helper::{extract_ndims, PrimDef};
use crate::toplevel::helper::PrimDef;
use crate::toplevel::numpy::{make_ndarray_ty, unpack_ndarray_var_tys};
use crate::typecheck::{
type_inferencer::*,
@ -13,8 +13,6 @@ use std::collections::HashMap;
use std::rc::Rc;
use strum::IntoEnumIterator;
use super::typedef::into_var_map;
/// The variant of a binary operator.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum BinopVariant {
@ -173,8 +171,19 @@ pub fn impl_binop(
ops: &[Operator],
) {
with_fields(unifier, ty, |unifier, fields| {
let other_tvar = unifier.get_fresh_var_with_range(other_ty, Some("N".into()), None);
let function_vars = into_var_map([other_tvar]);
let (other_ty, other_var_id) = if other_ty.len() == 1 {
(other_ty[0], None)
} else {
let tvar = unifier.get_fresh_var_with_range(other_ty, Some("N".into()), None);
(tvar.ty, Some(tvar.id))
};
let function_vars = if let Some(var_id) = other_var_id {
vec![(var_id, other_ty)].into_iter().collect::<VarMap>()
} else {
VarMap::new()
};
let ret_ty = ret_ty.unwrap_or_else(|| unifier.get_fresh_var(None, None).ty);
for (base_op, variant) in iproduct!(ops, [BinopVariant::Normal, BinopVariant::AugAssign]) {
@ -185,7 +194,7 @@ pub fn impl_binop(
ret: ret_ty,
vars: function_vars.clone(),
args: vec![FuncArg {
ty: other_tvar.ty,
ty: other_ty,
default_value: None,
name: "other".into(),
is_vararg: false,
@ -511,41 +520,53 @@ pub fn typeof_binop(
}
Operator::MatMult => {
let (lhs_dtype, lhs_ndims) = unpack_ndarray_var_tys(unifier, lhs);
let lhs_ndims = extract_ndims(unifier, lhs_ndims);
let (rhs_dtype, rhs_ndims) = unpack_ndarray_var_tys(unifier, rhs);
let rhs_ndims = extract_ndims(unifier, rhs_ndims);
if !(unifier.unioned(lhs_dtype, primitives.float)
&& unifier.unioned(rhs_dtype, primitives.float))
// NOTE: NumPy matmul's LHS and RHS must both be ndarrays. Scalars are not allowed.
match (&*unifier.get_ty(lhs), &*unifier.get_ty(rhs)) {
(
TypeEnum::TObj { obj_id: lhs_obj_id, .. },
TypeEnum::TObj { obj_id: rhs_obj_id, .. },
) if *lhs_obj_id == primitives.ndarray.obj_id(unifier).unwrap()
&& *rhs_obj_id == primitives.ndarray.obj_id(unifier).unwrap() =>
{
return Err(format!(
"ndarray.__matmul__ only supports float64 operations, but LHS has type {} and RHS has type {}",
unifier.stringify(lhs),
unifier.stringify(rhs)
));
// LHS and RHS have valid types
}
_ => {
let lhs_str = unifier.stringify(lhs);
let rhs_str = unifier.stringify(rhs);
return Err(format!("ndarray.__matmul__ only accepts ndarray operands, but left operand has type {lhs_str}, and right operand has type {rhs_str}"));
}
}
let result_ndims = match (lhs_ndims, rhs_ndims) {
(0, _) | (_, 0) => {
return Err(
"ndarray.__matmul__ does not allow unsized ndarray input".to_string()
)
let (_, lhs_ndims) = unpack_ndarray_var_tys(unifier, lhs);
let lhs_ndims = match &*unifier.get_ty_immutable(lhs_ndims) {
TypeEnum::TLiteral { values, .. } => {
assert_eq!(values.len(), 1);
u64::try_from(values[0].clone()).unwrap()
}
(1, 1) => 0,
(1, _) => rhs_ndims - 1,
(_, 1) => lhs_ndims - 1,
(m, n) => max(m, n),
_ => unreachable!(),
};
let (_, rhs_ndims) = unpack_ndarray_var_tys(unifier, rhs);
let rhs_ndims = match &*unifier.get_ty_immutable(rhs_ndims) {
TypeEnum::TLiteral { values, .. } => {
assert_eq!(values.len(), 1);
u64::try_from(values[0].clone()).unwrap()
}
_ => unreachable!(),
};
if result_ndims == 0 {
// If the result is unsized, NumPy returns a scalar.
primitives.float
} else {
let result_ndims_ty =
unifier.get_fresh_literal(vec![SymbolValue::U64(result_ndims)], None);
make_ndarray_ty(unifier, primitives, Some(primitives.float), Some(result_ndims_ty))
match (lhs_ndims, rhs_ndims) {
(2, 2) => typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?,
(lhs, rhs) if lhs == 0 || rhs == 0 => {
return Err(format!(
"Input operand {} does not have enough dimensions (has {lhs}, requires {rhs})",
u8::from(rhs == 0)
))
}
(lhs, rhs) => {
return Err(format!(
"ndarray.__matmul__ on {lhs}D and {rhs}D operands not supported"
))
}
}
}
@ -748,7 +769,7 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
impl_div(unifier, store, ndarray_t, &[ndarray_t, ndarray_dtype_t], None);
impl_floordiv(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
impl_mod(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
impl_matmul(unifier, store, ndarray_t, &[ndarray_unsized_t], None);
impl_matmul(unifier, store, ndarray_t, &[ndarray_t], Some(ndarray_t));
impl_sign(unifier, store, ndarray_t, Some(ndarray_t));
impl_invert(unifier, store, ndarray_t, Some(ndarray_t));
impl_eq(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);

172
nac3core/src/typecheck/type_inferencer/mod.rs
View File

@ -1,7 +1,7 @@
use std::cmp::max;
use std::collections::{HashMap, HashSet};
use std::convert::{From, TryInto};
use std::iter::{self, once};
use std::iter::once;
use std::{cell::RefCell, sync::Arc};
use super::{
@ -12,6 +12,7 @@ use super::{
RecordField, RecordKey, Type, TypeEnum, TypeVar, Unifier, VarMap,
},
};
use crate::toplevel::type_annotation::TypeAnnotation;
use crate::{
symbol_resolver::{SymbolResolver, SymbolValue},
toplevel::{
@ -102,6 +103,7 @@ pub struct Inferencer<'a> {
}
type InferenceError = HashSet<String>;
type OverrideResult = Result<Option<ast::Expr<Option<Type>>>, InferenceError>;
struct NaiveFolder();
impl Fold<()> for NaiveFolder {
@ -1181,45 +1183,6 @@ impl<'a> Inferencer<'a> {
}));
}
if ["np_shape".into(), "np_strides".into()].contains(id) && args.len() == 1 {
let ndarray = self.fold_expr(args.remove(0))?;
let ndims = arraylike_get_ndims(self.unifier, ndarray.custom.unwrap());
// Make a tuple of size `ndims` full of int32 (TODO: Make it usize)
let ret_ty = TypeEnum::TTuple {
ty: iter::repeat(self.primitives.int32).take(ndims as usize).collect_vec(),
is_vararg_ctx: false,
};
let ret_ty = self.unifier.add_ty(ret_ty);
let func_ty = TypeEnum::TFunc(FunSignature {
args: vec![FuncArg {
name: "a".into(),
default_value: None,
ty: ndarray.custom.unwrap(),
is_vararg: false,
}],
ret: ret_ty,
vars: VarMap::new(),
});
let func_ty = self.unifier.add_ty(func_ty);
return Ok(Some(Located {
location,
custom: Some(ret_ty),
node: ExprKind::Call {
func: Box::new(Located {
custom: Some(func_ty),
location: func.location,
node: ExprKind::Name { id: *id, ctx: *ctx },
}),
args: vec![ndarray],
keywords: vec![],
},
}));
}
if id == &"np_dot".into() {
let arg0 = self.fold_expr(args.remove(0))?;
let arg1 = self.fold_expr(args.remove(0))?;
@ -1541,7 +1504,7 @@ impl<'a> Inferencer<'a> {
}));
}
// 2-argument ndarray n-dimensional factory functions
if ["np_reshape".into(), "np_broadcast_to".into()].contains(id) && args.len() == 2 {
if id == &"np_reshape".into() && args.len() == 2 {
let arg0 = self.fold_expr(args.remove(0))?;
let shape_expr = args.remove(0);
@ -1587,36 +1550,29 @@ impl<'a> Inferencer<'a> {
}
// 2-argument ndarray n-dimensional creation functions
if id == &"np_full".into() && args.len() == 2 {
let ExprKind::List { elts, .. } = &args[0].node else {
return report_error(
format!(
"Expected List literal for first argument of {id}, got {}",
args[0].node.name()
)
.as_str(),
args[0].location,
);
};
// Parse arguments
let shape_expr = args.remove(0);
let (ndims, shape) =
self.fold_numpy_function_call_shape_argument(*id, 0, shape_expr)?; // Special handling for `shape`
let ndims = elts.len() as u64;
let fill_value = self.fold_expr(args.remove(0))?;
let arg0 = self.fold_expr(args.remove(0))?;
let arg1 = self.fold_expr(args.remove(0))?;
let ty = arg1.custom.unwrap();
// Build the return type
let dtype = fill_value.custom.unwrap();
let ndims = self.unifier.get_fresh_literal(vec![SymbolValue::U64(ndims)], None);
let ret = make_ndarray_ty(self.unifier, self.primitives, Some(ty), Some(ndims));
let ret = make_ndarray_ty(self.unifier, self.primitives, Some(dtype), Some(ndims));
let custom = self.unifier.add_ty(TypeEnum::TFunc(FunSignature {
args: vec![
FuncArg {
name: "shape".into(),
ty: arg0.custom.unwrap(),
ty: shape.custom.unwrap(),
default_value: None,
is_vararg: false,
},
FuncArg {
name: "fill_value".into(),
ty: arg1.custom.unwrap(),
ty: fill_value.custom.unwrap(),
default_value: None,
is_vararg: false,
},
@ -1634,7 +1590,7 @@ impl<'a> Inferencer<'a> {
location: func.location,
node: ExprKind::Name { id: *id, ctx: *ctx },
}),
args: vec![arg0, arg1],
args: vec![shape, fill_value],
keywords: vec![],
},
}));
@ -1711,6 +1667,86 @@ impl<'a> Inferencer<'a> {
Ok(None)
}
/// Checks whether a class method is calling parent function
/// Returns [`None`] if its not a call to parent method, otherwise
/// returns a new `func` with class name replaced by `self` and method resolved to its `DefinitionID`
///
/// e.g. A.f1(self, ...) returns Some(self.{DefintionID(f1)})
fn check_overriding(&mut self, func: &ast::Expr<()>, args: &[ast::Expr<()>]) -> OverrideResult {
// `self` must be first argument for call to parent method
if let Some(Located { node: ExprKind::Name { id, .. }, .. }) = &args.first() {
if *id != "self".into() {
return Ok(None);
}
} else {
return Ok(None);
}
let Located {
node: ExprKind::Attribute { value, attr: method_name, ctx }, location, ..
} = func
else {
return Ok(None);
};
let ExprKind::Name { id: class_name, ctx: class_ctx } = &value.node else {
return Ok(None);
};
let zelf = &self.fold_expr(args[0].clone())?;
// Check whether the method belongs to class ancestors
let def_id = self.unifier.get_ty(zelf.custom.unwrap());
let TypeEnum::TObj { obj_id, .. } = def_id.as_ref() else { unreachable!() };
let defs = self.top_level.definitions.read();
let res = {
if let TopLevelDef::Class { ancestors, .. } = &*defs[obj_id.0].read() {
let res = ancestors.iter().find_map(|f| {
let TypeAnnotation::CustomClass { id, .. } = f else { unreachable!() };
let TopLevelDef::Class { name, methods, .. } = &*defs[id.0].read() else {
unreachable!()
};
// Class names are stored as `__module__.class`
let name = name.to_string();
let (_, name) = name.rsplit_once('.').unwrap();
if name == class_name.to_string() {
return methods.iter().find_map(|f| {
if f.0 == *method_name {
return Some(*f);
}
None
});
}
None
});
res
} else {
None
}
};
match res {
Some(r) => {
let mut new_func = func.clone();
let mut new_value = value.clone();
new_value.node = ExprKind::Name { id: "self".into(), ctx: *class_ctx };
new_func.node =
ExprKind::Attribute { value: new_value.clone(), attr: *method_name, ctx: *ctx };
let mut new_func = self.fold_expr(new_func)?;
let ExprKind::Attribute { value, .. } = new_func.node else { unreachable!() };
new_func.node =
ExprKind::Attribute { value, attr: r.2 .0.to_string().into(), ctx: *ctx };
new_func.custom = Some(r.1);
Ok(Some(new_func))
}
None => report_error(
format!("Ancestor method [{class_name}.{method_name}] should be defined with same decorator as its overridden version").as_str(),
*location,
),
}
}
fn fold_call(
&mut self,
location: Location,
@ -1724,8 +1760,20 @@ impl<'a> Inferencer<'a> {
return Ok(spec_call_func);
}
let func = Box::new(self.fold_expr(func)?);
let args = args.into_iter().map(|v| self.fold_expr(v)).collect::<Result<Vec<_>, _>>()?;
// Check for call to parent method
let override_res = self.check_overriding(&func, &args)?;
let is_override = override_res.is_some();
let func = if is_override { override_res.unwrap() } else { self.fold_expr(func)? };
let func = Box::new(func);
let mut args =
args.into_iter().map(|v| self.fold_expr(v)).collect::<Result<Vec<_>, _>>()?;
// TODO: Handle passing of self to functions to allow runtime lookup of functions to be called
// Currently removing `self` and using compile time function definitions
if is_override {
args.remove(0);
}
let keywords = keywords
.into_iter()
.map(|v| fold::fold_keyword(self, v))

4
nac3core/src/typecheck/typedef/mod.rs
View File

@ -670,8 +670,8 @@ impl Unifier {
let num_args = posargs.len() + kwargs.len();
// Now we check the arguments against the parameters,
// and depending on what `call_info` is, we might change how the behavior `unify_call()`
// in hopes to improve user error messages when type checking fails.
// and depending on what `call_info` is, we might change how `unify_call()` behaves
// to improve user error messages when type checking fails.
match operator_info {
Some(OperatorInfo::IsBinaryOp { self_type, operator }) => {
// The call is written in the form of (say) `a + b`.

4
nac3parser/Cargo.toml
View File

@ -8,11 +8,11 @@ license = "MIT"
edition = "2021"
[build-dependencies]
lalrpop = "0.20"
lalrpop = "0.21"
[dependencies]
nac3ast = { path = "../nac3ast" }
lalrpop-util = "0.20"
lalrpop-util = "0.21"
log = "0.4"
unic-emoji-char = "0.9"
unic-ucd-ident = "0.9"

6
nac3standalone/Cargo.toml
View File

@ -9,14 +9,8 @@ no-escape-analysis = ["nac3core/no-escape-analysis"]
[dependencies]
parking_lot = "0.12"
nac3parser = { path = "../nac3parser" }
nac3core = { path = "../nac3core" }
[dependencies.clap]
version = "4.5"
features = ["derive"]
[dependencies.inkwell]
version = "0.4"
default-features = false
features = ["llvm14-0", "target-x86", "target-arm", "target-riscv", "no-libffi-linking"]

3
nac3standalone/demo/demo.c
View File

@ -120,7 +120,8 @@ struct Exception {
uint32_t __nac3_raise(struct Exception* e) {
printf("__nac3_raise called. Exception details:\n");
printf(" ID: %" PRIu32 "\n", e->id);
printf(" Location: %*s:%"PRIu32":%"PRIu32"\n" , (int) e->file.len, (const char*) e->file.data, e->line, e->column);
printf(" Location: %*s:%" PRIu32 ":%" PRIu32 "\n", (int)e->file.len, (const char*)e->file.data, e->line,
e->column);
printf(" Function: %*s\n", (int)e->function.len, (const char*)e->function.data);
printf(" Message: \"%*s\"\n", (int)e->message.len, (const char*)e->message.data);
printf(" Params: {0}=%" PRId64 ", {1}=%" PRId64 ", {2}=%" PRId64 "\n", e->param[0], e->param[1], e->param[2]);

12
nac3standalone/demo/interpret_demo.py
View File

@ -179,16 +179,6 @@ def patch(module):
module.np_identity = np.identity
module.np_array = np.array
# NumPy NDArray view functions
module.np_broadcast_to = np.broadcast_to
module.np_transpose = np.transpose
module.np_reshape = np.reshape
# NumPy NDArray property getters
module.np_size = np.size
module.np_shape = np.shape
module.np_strides = lambda ndarray: ndarray.strides
# NumPy Math functions
module.np_isnan = np.isnan
module.np_isinf = np.isinf
@ -228,6 +218,8 @@ def patch(module):
module.np_ldexp = np.ldexp
module.np_hypot = np.hypot
module.np_nextafter = np.nextafter
module.np_transpose = np.transpose
module.np_reshape = np.reshape
# SciPy Math functions
module.sp_spec_erf = special.erf

12
nac3standalone/demo/src/assignment.py
View File

@ -9,6 +9,7 @@ def output_bool(x: bool):
def example1():
x, *ys, z = (1, 2, 3, 4, 5)
output_int32(x)
output_int32(len(ys))
output_int32(ys[0])
output_int32(ys[1])
output_int32(ys[2])
@ -18,12 +19,14 @@ def example2():
x, y, *zs = (1, 2, 3, 4, 5)
output_int32(x)
output_int32(y)
output_int32(len(zs))
output_int32(zs[0])
output_int32(zs[1])
output_int32(zs[2])
def example3():
*xs, y, z = (1, 2, 3, 4, 5)
output_int32(len(xs))
output_int32(xs[0])
output_int32(xs[1])
output_int32(xs[2])
@ -31,6 +34,12 @@ def example3():
output_int32(z)
def example4():
*xs, y, z = (4, 5)
output_int32(len(xs))
output_int32(y)
output_int32(z)
def example5():
# Example from: https://docs.python.org/3/reference/simple_stmts.html#assignment-statements
x = [0, 1]
i = 0
@ -44,7 +53,7 @@ class A:
def __init__(self):
self.value = 1000
def example5():
def example6():
ws = [88, 7, 8]
a = A()
x, [y, *ys, a.value], ws[0], (ws[0],) = 1, (2, False, 4, 5), 99, (6,)
@ -63,4 +72,5 @@ def run() -> int32:
example3()
example4()
example5()
example6()
return 0

53
nac3standalone/demo/src/inheritance.py
View File

@ -10,23 +10,58 @@ class A:
def __init__(self, a: int32):
self.a = a
def f1(self):
self.f2()
def f2(self):
def output_all_fields(self):
output_int32(self.a)
def set_a(self, a: int32):
self.a = a
class B(A):
b: int32
def __init__(self, b: int32):
self.a = b + 1
A.__init__(self, b + 1)
self.set_b(b)
def output_parent_fields(self):
A.output_all_fields(self)
def output_all_fields(self):
A.output_all_fields(self)
output_int32(self.b)
def set_b(self, b: int32):
self.b = b
class C(B):
c: int32
def __init__(self, c: int32):
B.__init__(self, c + 1)
self.c = c
def output_parent_fields(self):
B.output_all_fields(self)
def output_all_fields(self):
B.output_all_fields(self)
output_int32(self.c)
def set_c(self, c: int32):
self.c = c
def run() -> int32:
aaa = A(5)
bbb = B(2)
aaa.f1()
bbb.f1()
ccc = C(10)
ccc.output_all_fields()
ccc.set_a(1)
ccc.set_b(2)
ccc.set_c(3)
ccc.output_all_fields()
bbb = B(10)
bbb.set_a(9)
bbb.set_b(8)
bbb.output_all_fields()
ccc.output_all_fields()
return 0

205
nac3standalone/demo/src/ndarray.py
View File

@ -68,19 +68,6 @@ def output_ndarray_float_2(n: ndarray[float, Literal[2]]):
for c in range(len(n[r])):
output_float64(n[r][c])
def output_ndarray_float_3(n: ndarray[float, Literal[3]]):
for d in range(len(n)):
for r in range(len(n[d])):
for c in range(len(n[d][r])):
output_float64(n[d][r][c])
def output_ndarray_float_4(n: ndarray[float, Literal[4]]):
for x in range(len(n)):
for y in range(len(n[x])):
for z in range(len(n[x][y])):
for w in range(len(n[x][y][z])):
output_float64(n[x][y][z][w])
def consume_ndarray_1(n: ndarray[float, Literal[1]]):
pass
@ -127,12 +114,22 @@ def test_ndarray_ones():
n: ndarray[float, 1] = np_ones([1])
output_ndarray_float_1(n)
dim = (1,)
n_tup: ndarray[float, 1] = np_ones(dim)
output_ndarray_float_1(n_tup)
def test_ndarray_full():
n_float: ndarray[float, 1] = np_full([1], 2.0)
output_ndarray_float_1(n_float)
n_i32: ndarray[int32, 1] = np_full([1], 2)
output_ndarray_int32_1(n_i32)
dim = (1,)
n_float_tup: ndarray[float, 1] = np_full(dim, 2.0)
output_ndarray_float_1(n_float_tup)
n_i32_tup: ndarray[int32, 1] = np_full(dim, 2)
output_ndarray_int32_1(n_i32_tup)
def test_ndarray_eye():
n: ndarray[float, 2] = np_eye(2)
output_ndarray_float_2(n)
@ -199,104 +196,6 @@ def test_ndarray_nd_idx():
output_float64(x[1, 0])
output_float64(x[1, 1])
def test_ndarray_transpose():
x: ndarray[float, 2] = np_array([[1., 2., 3.], [4., 5., 6.]])
y = np_transpose(x)
z = np_transpose(y)
output_int32(np_shape(x)[0])
output_int32(np_shape(x)[1])
output_ndarray_float_2(x)
output_int32(np_shape(y)[0])
output_int32(np_shape(y)[1])
output_ndarray_float_2(y)
output_int32(np_shape(z)[0])
output_int32(np_shape(z)[1])
output_ndarray_float_2(z)
def test_ndarray_reshape():
w: ndarray[float, 1] = np_array([1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
x = np_reshape(w, (1, 2, 1, -1))
y = np_reshape(x, [2, -1])
z = np_reshape(y, 10)
output_int32(np_shape(w)[0])
output_ndarray_float_1(w)
output_int32(np_shape(x)[0])
output_int32(np_shape(x)[1])
output_int32(np_shape(x)[2])
output_int32(np_shape(x)[3])
output_ndarray_float_4(x)
output_int32(np_shape(y)[0])
output_int32(np_shape(y)[1])
output_ndarray_float_2(y)
output_int32(np_shape(z)[0])
output_ndarray_float_1(z)
x1: ndarray[int32, 1] = np_array([1, 2, 3, 4])
x2: ndarray[int32, 2] = np_reshape(x1, (2, 2))
output_int32(np_shape(x1)[0])
output_ndarray_int32_1(x1)
output_int32(np_shape(x2)[0])
output_int32(np_shape(x2)[1])
output_ndarray_int32_2(x2)
def test_ndarray_broadcast_to():
xs = np_array([1.0, 2.0, 3.0])
ys = np_broadcast_to(xs, (1, 3))
zs = np_broadcast_to(ys, (2, 4, 3))
output_int32(np_shape(xs)[0])
output_ndarray_float_1(xs)
output_int32(np_shape(ys)[0])
output_int32(np_shape(ys)[1])
output_ndarray_float_2(ys)
output_int32(np_shape(zs)[0])
output_int32(np_shape(zs)[1])
output_int32(np_shape(zs)[2])
output_ndarray_float_3(zs)
def test_ndarray_subscript_assignment():
xs = np_array([[11.0, 22.0, 33.0, 44.0], [55.0, 66.0, 77.0, 88.0]])
xs[0, 0] = 99.0
output_ndarray_float_2(xs)
xs[0] = 100.0
output_ndarray_float_2(xs)
xs[:, ::2] = 101.0
output_ndarray_float_2(xs)
xs[1:, 0] = 102.0
output_ndarray_float_2(xs)
xs[0] = np_array([-1.0, -2.0, -3.0, -4.0])
output_ndarray_float_2(xs)
xs[:] = np_array([-5.0, -6.0, -7.0, -8.0])
output_ndarray_float_2(xs)
# Test assignment with memory sharing
ys1 = np_reshape(xs, (2, 4))
ys2 = np_transpose(ys1)
ys3 = ys2[::-1, 0]
ys3[0] = -999.0
output_ndarray_float_2(xs)
output_ndarray_float_2(ys1)
output_ndarray_float_2(ys2)
output_ndarray_float_1(ys3)
def test_ndarray_add():
x = np_identity(2)
y = x + np_ones([2, 2])
@ -641,59 +540,11 @@ def test_ndarray_ipow_broadcast_scalar():
output_ndarray_float_2(x)
def test_ndarray_matmul():
# 2D @ 2D -> 2D
a1 = np_array([[2.0, 3.0], [5.0, 7.0]])
b1 = np_array([[11.0, 13.0], [17.0, 23.0]])
c1 = a1 @ b1
output_int32(np_shape(c1)[0])
output_int32(np_shape(c1)[1])
output_ndarray_float_2(c1)
x = np_identity(2)
y = x @ np_ones([2, 2])
# 1D @ 1D -> Scalar
a2 = np_array([2.0, 3.0, 5.0])
b2 = np_array([7.0, 11.0, 13.0])
c2 = a2 @ b2
output_float64(c2)
# 2D @ 1D -> 1D
a3 = np_array([[1.0, 2.0, 3.0], [7.0, 8.0, 9.0]])
b3 = np_array([4.0, 5.0, 6.0])
c3 = a3 @ b3
output_int32(np_shape(c3)[0])
output_ndarray_float_1(c3)
# 1D @ 2D -> 1D
a4 = np_array([1.0, 2.0, 3.0])
b4 = np_array([[4.0, 5.0], [6.0, 7.0], [8.0, 9.0]])
c4 = a4 @ b4
output_int32(np_shape(c4)[0])
output_ndarray_float_1(c4)
# Broadcasting
a5 = np_array([
[[ 0.0, 1.0, 2.0, 3.0],
[ 4.0, 5.0, 6.0, 7.0]],
[[ 8.0, 9.0, 10.0, 11.0],
[12.0, 13.0, 14.0, 15.0]],
[[16.0, 17.0, 18.0, 19.0],
[20.0, 21.0, 22.0, 23.0]]
])
b5 = np_array([
[[[ 0.0, 1.0, 2.0],
[ 3.0, 4.0, 5.0],
[ 6.0, 7.0, 8.0],
[ 9.0, 10.0, 11.0]]],
[[[12.0, 13.0, 14.0],
[15.0, 16.0, 17.0],
[18.0, 19.0, 20.0],
[21.0, 22.0, 23.0]]]
])
c5 = a5 @ b5
output_int32(np_shape(c5)[0])
output_int32(np_shape(c5)[1])
output_int32(np_shape(c5)[2])
output_int32(np_shape(c5)[3])
output_ndarray_float_4(c5)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_imatmul():
x = np_identity(2)
@ -1588,6 +1439,27 @@ def test_ndarray_nextafter_broadcast_rhs_scalar():
output_ndarray_float_2(nextafter_x_zeros)
output_ndarray_float_2(nextafter_x_ones)
def test_ndarray_transpose():
x: ndarray[float, 2] = np_array([[1., 2., 3.], [4., 5., 6.]])
y = np_transpose(x)
z = np_transpose(y)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_reshape():
w: ndarray[float, 1] = np_array([1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
x = np_reshape(w, (1, 2, 1, -1))
y = np_reshape(x, [2, -1])
z = np_reshape(y, 10)
x1: ndarray[int32, 1] = np_array([1, 2, 3, 4])
x2: ndarray[int32, 2] = np_reshape(x1, (2, 2))
output_ndarray_float_1(w)
output_ndarray_float_2(y)
output_ndarray_float_1(z)
def test_ndarray_dot():
x1: ndarray[float, 1] = np_array([5.0, 1.0, 4.0, 2.0])
y1: ndarray[float, 1] = np_array([5.0, 1.0, 6.0, 6.0])
@ -1719,11 +1591,6 @@ def run() -> int32:
test_ndarray_slices()
test_ndarray_nd_idx()
test_ndarray_transpose()
test_ndarray_reshape()
test_ndarray_broadcast_to()
test_ndarray_subscript_assignment()
test_ndarray_add()
test_ndarray_add_broadcast()
test_ndarray_add_broadcast_lhs_scalar()
@ -1887,6 +1754,8 @@ def run() -> int32:
test_ndarray_nextafter_broadcast()
test_ndarray_nextafter_broadcast_lhs_scalar()
test_ndarray_nextafter_broadcast_rhs_scalar()
test_ndarray_transpose()
test_ndarray_reshape()
test_ndarray_dot()
test_ndarray_cholesky()

3
nac3standalone/src/basic_symbol_resolver.rs
View File

@ -1,3 +1,4 @@
use nac3core::nac3parser::ast::{self, StrRef};
use nac3core::{
codegen::CodeGenContext,
symbol_resolver::{SymbolResolver, SymbolValue, ValueEnum},
@ -7,7 +8,6 @@ use nac3core::{
typedef::{Type, Unifier},
},
};
use nac3parser::ast::{self, StrRef};
use parking_lot::{Mutex, RwLock};
use std::collections::HashSet;
use std::{collections::HashMap, sync::Arc};
@ -15,7 +15,6 @@ use std::{collections::HashMap, sync::Arc};
pub struct ResolverInternal {
pub id_to_type: Mutex<HashMap<StrRef, Type>>,
pub id_to_def: Mutex<HashMap<StrRef, DefinitionId>>,
pub class_names: Mutex<HashMap<StrRef, Type>>,
pub module_globals: Mutex<HashMap<StrRef, SymbolValue>>,
pub str_store: Mutex<HashMap<String, i32>>,
}

45
nac3standalone/src/main.rs
View File

@ -9,12 +9,14 @@
#![allow(clippy::too_many_lines, clippy::wildcard_imports)]
use clap::Parser;
use inkwell::context::Context;
use inkwell::{
memory_buffer::MemoryBuffer, passes::PassBuilderOptions, support::is_multithreaded, targets::*,
OptimizationLevel,
use nac3core::inkwell::{
memory_buffer::MemoryBuffer, module::Linkage, passes::PassBuilderOptions,
support::is_multithreaded, targets::*, OptimizationLevel,
};
use nac3core::nac3parser::{
ast::{Constant, Expr, ExprKind, StmtKind, StrRef},
parser,
};
use nac3core::codegen::irrt::setup_irrt_exceptions;
use nac3core::{
codegen::{
concrete_type::ConcreteTypeStore, irrt::load_irrt, CodeGenLLVMOptions,
@ -32,10 +34,6 @@ use nac3core::{
typedef::{FunSignature, Type, Unifier, VarMap},
},
};
use nac3parser::{
ast::{Constant, Expr, ExprKind, StmtKind, StrRef},
parser,
};
use parking_lot::{Mutex, RwLock};
use std::collections::HashSet;
use std::num::NonZeroUsize;
@ -281,16 +279,14 @@ fn main() {
reloc_mode: RelocMode::PIC,
..host_target_machine
};
let size_t = Context::create()
.ptr_sized_int_type(
&target_machine_options
let target_machine = target_machine_options
.create_target_machine(opt_level)
.map(|tm| tm.get_target_data())
.unwrap(),
None,
)
.get_bit_width();
.expect("couldn't create target machine");
let context = nac3core::inkwell::context::Context::create();
let size_t =
context.ptr_sized_int_type(&target_machine.get_target_data(), None).get_bit_width();
let program = match fs::read_to_string(file_name.clone()) {
Ok(program) => program,
@ -307,7 +303,6 @@ fn main() {
let internal_resolver: Arc<ResolverInternal> = ResolverInternal {
id_to_type: builtins_ty.into(),
id_to_def: builtins_def.into(),
class_names: Mutex::default(),
module_globals: Mutex::default(),
str_store: Mutex::default(),
}
@ -315,11 +310,8 @@ fn main() {
let resolver =
Arc::new(Resolver(internal_resolver.clone())) as Arc<dyn SymbolResolver + Send + Sync>;
let context = inkwell::context::Context::create();
// Process IRRT
let irrt = load_irrt(&context);
setup_irrt_exceptions(&context, &irrt, resolver.as_ref());
let irrt = load_irrt(&context, resolver.as_ref());
if emit_llvm {
irrt.write_bitcode_to_path(Path::new("irrt.bc"));
}
@ -456,17 +448,12 @@ fn main() {
let mut function_iter = main.get_first_function();
while let Some(func) = function_iter {
if func.count_basic_blocks() > 0 && func.get_name().to_str().unwrap() != "run" {
func.set_linkage(inkwell::module::Linkage::Private);
func.set_linkage(Linkage::Private);
}
function_iter = func.get_next_function();
}
// Optimize `main`
let target_machine = llvm_options
.target
.create_target_machine(llvm_options.opt_level)
.expect("couldn't create target machine");
let pass_options = PassBuilderOptions::create();
pass_options.set_merge_functions(true);
let passes = format!("default<O{}>", opt_level as u32);

4
nix/windows/PKGBUILD
View File

@ -21,6 +21,6 @@ build() {
}
package() {
mkdir -p $pkgdir/clang64/lib/python3.11/site-packages
cp ${srcdir}/nac3artiq.pyd $pkgdir/clang64/lib/python3.11/site-packages
mkdir -p $pkgdir/clang64/lib/python3.12/site-packages
cp ${srcdir}/nac3artiq.pyd $pkgdir/clang64/lib/python3.12/site-packages
}

4
nix/windows/default.nix
View File

@ -21,10 +21,10 @@ let
text =
''
implementation=CPython
version=3.11
version=3.12
shared=true
abi3=false
lib_name=python3.11
lib_name=python3.12
lib_dir=${msys2-env}/clang64/lib
pointer_width=64
build_flags=WITH_THREAD

4
nix/windows/make_msys2_packages.sh
View File

@ -6,11 +6,11 @@ cd $(dirname $0)
MSYS2DIR=`pwd`/msys2
mkdir -p $MSYS2DIR/var/lib/pacman $MSYS2DIR/msys/etc
curl -L https://mirror.msys2.org/msys/x86_64/pacman-mirrors-20220205-1-any.pkg.tar.zst | tar xvf - -C $MSYS2DIR --zstd
curl -L https://repo.msys2.org/msys/x86_64/pacman-mirrors-20240523-1-any.pkg.tar.zst | tar xvf - -C $MSYS2DIR --zstd
curl -L https://raw.githubusercontent.com/msys2/MSYS2-packages/master/pacman/pacman.conf | sed "s|SigLevel = Required|SigLevel = Never|g" | sed "s|/etc/pacman.d|$MSYS2DIR/etc/pacman.d|g" > $MSYS2DIR/etc/pacman.conf
fakeroot pacman --root $MSYS2DIR --config $MSYS2DIR/etc/pacman.conf -Syy
pacman --root $MSYS2DIR --config $MSYS2DIR/etc/pacman.conf --cachedir $MSYS2DIR/msys/cache -Sp mingw-w64-clang-x86_64-rust mingw-w64-clang-x86_64-cmake mingw-w64-clang-x86_64-ninja mingw-w64-clang-x86_64-python3.11 mingw-w64-clang-x86_64-python-numpy mingw-w64-clang-x86_64-python-setuptools > $MSYS2DIR/packages.txt
pacman --root $MSYS2DIR --config $MSYS2DIR/etc/pacman.conf --cachedir $MSYS2DIR/msys/cache -Sp mingw-w64-clang-x86_64-rust mingw-w64-clang-x86_64-cmake mingw-w64-clang-x86_64-ninja mingw-w64-clang-x86_64-python3.12 mingw-w64-clang-x86_64-python-numpy mingw-w64-clang-x86_64-python-setuptools > $MSYS2DIR/packages.txt
echo "{ pkgs } : [" > msys2_packages.nix
while read package; do

150
nix/windows/msys2_packages.nix
View File

@ -1,15 +1,15 @@
{ pkgs } : [
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libunwind-18.1.8-1-any.pkg.tar.zst";
sha256 = "1v8zkfcbf1ga2ndpd1j0dwv5s1rassxs2b5pjhcsmqwjcvczba1m";
name = "mingw-w64-clang-x86_64-libunwind-18.1.8-1-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libunwind-18.1.8-2-any.pkg.tar.zst";
sha256 = "0f9m76dx40iy794nfks0360gvjhdg6yngb2lyhwp4xd76rn5081m";
name = "mingw-w64-clang-x86_64-libunwind-18.1.8-2-any.pkg.tar.zst";
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libc++-18.1.8-1-any.pkg.tar.zst";
sha256 = "0mfd8wrmgx12j5gf354j7pk1l3lg9ykxvq75xdk3jipsr6hbn846";
name = "mingw-w64-clang-x86_64-libc++-18.1.8-1-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libc++-18.1.8-2-any.pkg.tar.zst";
sha256 = "17savj9wys9my2ji7vyba7wwqkvzdjwnkb3k4858wxrjbzbfa6lk";
name = "mingw-w64-clang-x86_64-libc++-18.1.8-2-any.pkg.tar.zst";
})
(pkgs.fetchurl {
@ -43,9 +43,9 @@
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libxml2-2.12.8-1-any.pkg.tar.zst";
sha256 = "1imipb0dz4w6x4n9arn22imyzzcwdlf2cqxvn7irqq7w9by6fy0b";
name = "mingw-w64-clang-x86_64-libxml2-2.12.8-1-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libxml2-2.12.9-1-any.pkg.tar.zst";
sha256 = "0cjz2vj9yz6k5xj601cp0yk631rrr0z94ciamwqrvclb0yhakf25";
name = "mingw-w64-clang-x86_64-libxml2-2.12.9-1-any.pkg.tar.zst";
})
(pkgs.fetchurl {
@ -79,15 +79,15 @@
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-headers-git-12.0.0.r81.g90abf784a-1-any.pkg.tar.zst";
sha256 = "1h3cdcajz29iq7vja908kkijz1vb9xn0f7w1lw1ima0q0zhinv4q";
name = "mingw-w64-clang-x86_64-headers-git-12.0.0.r81.g90abf784a-1-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-headers-git-12.0.0.r250.gc6bf4bdf6-1-any.pkg.tar.zst";
sha256 = "0163jzjlvq7inpafy3h48pkwag3ysk6x56xm84yfcz5q52fnfzq5";
name = "mingw-w64-clang-x86_64-headers-git-12.0.0.r250.gc6bf4bdf6-1-any.pkg.tar.zst";
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-crt-git-12.0.0.r81.g90abf784a-1-any.pkg.tar.zst";
sha256 = "15kamyi3b0j6f5zxin4i2jgzjc7lzvwl4z5cz3dx0i8hg91aq0n7";
name = "mingw-w64-clang-x86_64-crt-git-12.0.0.r81.g90abf784a-1-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-crt-git-12.0.0.r250.gc6bf4bdf6-1-any.pkg.tar.zst";
sha256 = "00cn1mi29mfys7qy4hvgnjd0smqvnkdn3ibnrr6a3wy1h2vaykgq";
name = "mingw-w64-clang-x86_64-crt-git-12.0.0.r250.gc6bf4bdf6-1-any.pkg.tar.zst";
})
(pkgs.fetchurl {
@ -97,15 +97,15 @@
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libwinpthread-git-12.0.0.r81.g90abf784a-1-any.pkg.tar.zst";
sha256 = "0qdvgs1rmjjhn9klf9kpw7l0ydz36rr5fasn4q9gpby2lgl11bkb";
name = "mingw-w64-clang-x86_64-libwinpthread-git-12.0.0.r81.g90abf784a-1-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libwinpthread-git-12.0.0.r250.gc6bf4bdf6-1-any.pkg.tar.zst";
sha256 = "1zkzqqd31xpkv817wja3qssjjx891bsdxw07037hv2sk0qr4ffn9";
name = "mingw-w64-clang-x86_64-libwinpthread-git-12.0.0.r250.gc6bf4bdf6-1-any.pkg.tar.zst";
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-winpthreads-git-12.0.0.r81.g90abf784a-1-any.pkg.tar.zst";
sha256 = "0rh2mn078cifcmr4as4k57jxjln5lbnsmpx47h9d0s5d2i8sf2rc";
name = "mingw-w64-clang-x86_64-winpthreads-git-12.0.0.r81.g90abf784a-1-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-winpthreads-git-12.0.0.r250.gc6bf4bdf6-1-any.pkg.tar.zst";
sha256 = "02ynia88ad3l03r08nyldmnajwqkyxcjd191lyamkbj4d6zck323";
name = "mingw-w64-clang-x86_64-winpthreads-git-12.0.0.r250.gc6bf4bdf6-1-any.pkg.tar.zst";
})
(pkgs.fetchurl {
@ -115,15 +115,15 @@
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-c-ares-1.29.0-1-any.pkg.tar.zst";
sha256 = "01xg1h1a8kda0kq2921w25ybvm1ms7lfdzday0hv93f3myq7briq";
name = "mingw-w64-clang-x86_64-c-ares-1.29.0-1-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-c-ares-1.33.1-1-any.pkg.tar.zst";
sha256 = "14r6jjsvfbapbkv2zqp2yglva4vz4srzkgk7f186ri3kcafjspgq";
name = "mingw-w64-clang-x86_64-c-ares-1.33.1-1-any.pkg.tar.zst";
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-brotli-1.1.0-1-any.pkg.tar.zst";
sha256 = "113mha41q53cx0hw13cq1xdf7zbsd58sh8cl1cd7xzg1q69n60w2";
name = "mingw-w64-clang-x86_64-brotli-1.1.0-1-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-brotli-1.1.0-2-any.pkg.tar.zst";
sha256 = "1q01lz9lcyrjmkhv9rddgjazmk7warlcmwhc4qkq9y6h0yfsb71n";
name = "mingw-w64-clang-x86_64-brotli-1.1.0-2-any.pkg.tar.zst";
})
(pkgs.fetchurl {
@ -163,9 +163,9 @@
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-openssl-3.3.1-1-any.pkg.tar.zst";
sha256 = "0ywhwm4kw3qjzv0872qwabnsq2rzbmqjb9m69q3fykjl0m9gigsa";
name = "mingw-w64-clang-x86_64-openssl-3.3.1-1-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-openssl-3.3.2-1-any.pkg.tar.zst";
sha256 = "1djgpcz447yvhdy1yq5wh8l5d0821izxklx9afyszbw0pbr7f24y";
name = "mingw-w64-clang-x86_64-openssl-3.3.2-1-any.pkg.tar.zst";
})
(pkgs.fetchurl {
@ -175,39 +175,39 @@
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-nghttp2-1.61.0-2-any.pkg.tar.zst";
sha256 = "07bkk98126gy4k6lb9rrqqnzjfz9j2rsr5dzr2djmzdkw0h4dr95";
name = "mingw-w64-clang-x86_64-nghttp2-1.61.0-2-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-nghttp2-1.63.0-1-any.pkg.tar.zst";
sha256 = "0lfqrlmapsc7ilxjmhr7hxi578vclqlhpqimbvzq0c70c0iwk864";
name = "mingw-w64-clang-x86_64-nghttp2-1.63.0-1-any.pkg.tar.zst";
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-nghttp3-1.4.0-1-any.pkg.tar.zst";
sha256 = "007w2252nzn274j4wjc1vf56xyzzh5vg3blj1hil7mlmffgvc923";
name = "mingw-w64-clang-x86_64-nghttp3-1.4.0-1-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-nghttp3-1.5.0-1-any.pkg.tar.zst";
sha256 = "1ljl9kdasf91bxkqcmbbjchp5g00ahv8jn2zab38899z6j3x43nz";
name = "mingw-w64-clang-x86_64-nghttp3-1.5.0-1-any.pkg.tar.zst";
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-curl-8.8.0-10-any.pkg.tar.zst";
sha256 = "024z5b1achkf448gxqy1i3gcw371x54kfl6igv08b5wb3rrw35a4";
name = "mingw-w64-clang-x86_64-curl-8.8.0-10-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-curl-8.9.1-2-any.pkg.tar.zst";
sha256 = "1zr6kgqp9i4qqrfckh3kfmz4x1cwv4xis9sfqsx7xji88priax64";
name = "mingw-w64-clang-x86_64-curl-8.9.1-2-any.pkg.tar.zst";
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-rust-1.79.0-1-any.pkg.tar.zst";
sha256 = "0i7s88hj8m4920xifkj7i4b4sq8cqq7p5cypp3jqx3dc44pwm19a";
name = "mingw-w64-clang-x86_64-rust-1.79.0-1-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-rust-1.80.1-1-any.pkg.tar.zst";
sha256 = "1dm4vlrfi9m6xl09zpn0yjr7qcjjr4x738z1rjfwysfnc0awq4x8";
name = "mingw-w64-clang-x86_64-rust-1.80.1-1-any.pkg.tar.zst";
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-pkgconf-1~2.2.0-1-any.pkg.tar.zst";
sha256 = "1y44ijg3y8p80f1yn9972nshrnyrd06a9sh984ajhxg8bi8s5xyl";
name = "mingw-w64-clang-x86_64-pkgconf-12.2.0-1-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-cppdap-1.65-1-any.pkg.tar.zst";
sha256 = "0phhwkcqp30dsyj5vr6w99sgm1jfm5rzg0w5x5mv9md4x7lm9lmh";
name = "mingw-w64-clang-x86_64-cppdap-1.65-1-any.pkg.tar.zst";
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-expat-2.6.2-1-any.pkg.tar.zst";
sha256 = "0kj1vzjh3qh7d2g47avlgk7a6j4nc62111hy1m63jwq0alc01k38";
name = "mingw-w64-clang-x86_64-expat-2.6.2-1-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-expat-2.6.3-1-any.pkg.tar.zst";
sha256 = "19xfl1q78q1k8j0lr5aspcf668pmfg01fgib73zq7ff7y5y5fcyi";
name = "mingw-w64-clang-x86_64-expat-2.6.3-1-any.pkg.tar.zst";
})
(pkgs.fetchurl {
@ -229,9 +229,9 @@
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-lz4-1.9.4-1-any.pkg.tar.zst";
sha256 = "0nn7cy25j53q5ckkx4n4f77w00xdwwf5wjswm374shvvs58nlln0";
name = "mingw-w64-clang-x86_64-lz4-1.9.4-1-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-lz4-1.10.0-1-any.pkg.tar.zst";
sha256 = "0kznnw9z9zqxkmn8qbypm2rpsfaapbgls1ks3zzpfnfjz9cpw8py";
name = "mingw-w64-clang-x86_64-lz4-1.10.0-1-any.pkg.tar.zst";
})
(pkgs.fetchurl {
@ -264,6 +264,12 @@
name = "mingw-w64-clang-x86_64-ninja-1.12.1-1-any.pkg.tar.zst";
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-pkgconf-1~2.3.0-1-any.pkg.tar.zst";
sha256 = "15i7x6akkgs7aa7aa804k93p2iipnvygsy7z8hsafskka3h150fa";
name = "mingw-w64-clang-x86_64-pkgconf-12.3.0-1-any.pkg.tar.zst";
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-rhash-1.4.4-3-any.pkg.tar.zst";
sha256 = "1ysbxirpfr0yf7pvyps75lnwc897w2a2kcid3nb4j6ilw6n64jmc";
@ -271,9 +277,9 @@
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-cmake-3.30.0-1-any.pkg.tar.zst";
sha256 = "07b7132hwhiqrf0l2lgw3g4zw9i2lln3kqc9kg2qijvkapbkmwqb";
name = "mingw-w64-clang-x86_64-cmake-3.30.0-1-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-cmake-3.30.3-1-any.pkg.tar.zst";
sha256 = "0fjwf6xxzli6rcsbzr1razldmm538ibkyf5kw132lpaz5wma9bj8";
name = "mingw-w64-clang-x86_64-cmake-3.30.3-1-any.pkg.tar.zst";
})
(pkgs.fetchurl {
@ -295,9 +301,9 @@
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-readline-8.2.010-1-any.pkg.tar.zst";
sha256 = "1s47pd5iz8y3hspsxn4pnp0v3m05ccia40v5nfvx0rmwgvcaz82v";
name = "mingw-w64-clang-x86_64-readline-8.2.010-1-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-readline-8.2.013-1-any.pkg.tar.zst";
sha256 = "0pv1ypqfgm4mimzr0amq9anr1ysqmzrwv6gfk7rrlzhihadknsvr";
name = "mingw-w64-clang-x86_64-readline-8.2.013-1-any.pkg.tar.zst";
})
(pkgs.fetchurl {
@ -307,9 +313,9 @@
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-sqlite3-3.46.0-1-any.pkg.tar.zst";
sha256 = "0q676i2z5nr4c71jnd4z5qz9xa1xryl0cpi84w74yvd0p4qiz7y2";
name = "mingw-w64-clang-x86_64-sqlite3-3.46.0-1-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-sqlite3-3.46.1-1-any.pkg.tar.zst";
sha256 = "1axplxyjnaz411qzjjqwbj55fbrh4akq3plm2p1sx64jp844xpyq";
name = "mingw-w64-clang-x86_64-sqlite3-3.46.1-1-any.pkg.tar.zst";
})
(pkgs.fetchurl {
@ -324,6 +330,12 @@
name = "mingw-w64-clang-x86_64-tzdata-2024a-1-any.pkg.tar.zst";
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-python3.12-3.12.1-2-any.pkg.tar.zst";
sha256 = "0wmd39wl9z237w093a7c6hl5pclca9yvwxn0kiw6i2njk3sjv51a";
name = "mingw-w64-clang-x86_64-python3.12-3.12.1-2-any.pkg.tar.zst";
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-python-3.11.9-1-any.pkg.tar.zst";
sha256 = "0ah1idjqxg7jc07a1gz9z766rjjd0f0c6ri4hpcsimsrbj1zjd3c";
@ -337,20 +349,20 @@
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-openblas-0.3.27-1-any.pkg.tar.zst";
sha256 = "06ygz1wa488wqvmxbn74b0fyan4wf3lb6kbwfampgikd1gijww2k";
name = "mingw-w64-clang-x86_64-openblas-0.3.27-1-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-openblas-0.3.28-1-any.pkg.tar.zst";
sha256 = "1pskcqc1lg9p8m8rk7bw3mz7mn7vw5fpl7zxa23bhjn02p5b79qq";
name = "mingw-w64-clang-x86_64-openblas-0.3.28-1-any.pkg.tar.zst";
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-python-numpy-1.26.4-1-any.pkg.tar.zst";
sha256 = "00h0ap954cjwlsc3p01fjwy7s3nlzs90v0kmnrzxm0rljmvn4jkf";
name = "mingw-w64-clang-x86_64-python-numpy-1.26.4-1-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-python-numpy-2.0.1-1-any.pkg.tar.zst";
sha256 = "0ks6q8v58h4wmr2pzsjl2xm4f63g0psvfm0jwlz24mqfxp8gqfcc";
name = "mingw-w64-clang-x86_64-python-numpy-2.0.1-1-any.pkg.tar.zst";
})
(pkgs.fetchurl {
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-python-setuptools-70.2.0-1-any.pkg.tar.zst";
sha256 = "1q4r9bg2hn3jmshvq81xm5zvy9wn35yf0z2ayksrkwph1zzdkvkm";
name = "mingw-w64-clang-x86_64-python-setuptools-70.2.0-1-any.pkg.tar.zst";
url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-python-setuptools-74.0.0-1-any.pkg.tar.zst";
sha256 = "0xc95z5jzzjf5lw35bs4yn5rlwkrkmrh78yi5rranqpz5nn0wsa0";
name = "mingw-w64-clang-x86_64-python-setuptools-74.0.0-1-any.pkg.tar.zst";
})
]